An international consensus based on the Delphi method to define failure of medical treatment in pyogenic spinal infections.

BACKGROUND CONTEXT: Pyogenic spinal infections (PSIs) are severe conditions with high morbidity and mortality. If medical treatment fails, patients may require surgery, but there is no consensus regarding the definition of medical treatment failure. PURPOSE: To determine criteria for defining failure of medical treatment in PSI through an international consensus of experts. STUDY DESIGN: A two-round basic Delphi method study. SAMPLE: One hundred and fifty experts from 22 countries (authors or co-authors of clinical guidelines or indexed publications on the topic) were invited to participate; 33 answered both rounds defining the criteria. OUTCOME MEASURES: A scale of 1 to 9 (1: no relevance; 9: highly relevant) applied to each criterion. METHODS: We created an online survey with 10 criteria reported in the literature to define the failure of medical treatment in PSIs. We sent this survey via email to the experts. Agreement among the participants on relevant criteria (score ≥7) was determined. One month later, the second round of evaluations was sent. An extra criterion suggested by six responders in the first round was incorporated. The final version was reached with the criteria considered relevant and with high agreement. RESULTS: The consensus definition is: (1) There is an uncontrolled sepsis despite broad spectrum antibiotic treatment, and (2) There is an infection relapse, following a six-week period of antibiotics with clinical and laboratory improvement. CONCLUSIONS: Our definition of failure following nonsurgical treatment of PSI can offer a standardized approach to guide clinical decision-making. Furthermore, it has the potential to enhance scientific reporting within this field.

Predicting Prolonged Hospital Stays in Elderly Patients With Hip Fractures Managed During the COVID-19 Pandemic in Chile: An Artificial Neural Networks Study.

Background: Prolonged length of stay (LOS) after a hip fracture is associated with increased mortality. Purpose: We sought to create a model to predict prolonged LOS in elderly Chilean patients with hip fractures managed during the COVID-19 pandemic. Methods: Employing an official database, we created an artificial neural network (ANN), a computational model corresponding to a subset of machine learning, to predict prolonged LOS (≥14 days) among 2686 hip fracture patients managed in 43 Chilean public hospitals during 2020. We identified 18 clinically relevant variables as potential predictors; 80% of the sample was used to train the ANN and 20% was used to test it. The performance of the ANN was evaluated via measuring its discrimination power through the area under the curve of the receiver operating characteristic curve (AUC-ROC). Results: Of the 2686 patients, 820 (30.2%) had prolonged LOS. In the training sample (2,125 cases), the ANN correctly classified 1,532 cases (72.09%; AUC-ROC: 0.745). In the test sample (561 cases), the ANN correctly classified 401 cases (71.48%; AUC-ROC: 0.742). The most relevant variables to predict prolonged LOS were the patient's admitting hospital (relative importance [RI]: 0.11), the patient's geographical health service providing health care (RI: 0.11), and the patient's surgery being conducted within 2 days of admission (RI: 0.10). Conclusions: Using national-level big data, we developed an ANN that predicted with fair accuracy prolonged LOS in elderly Chilean patients with hip fractures during the COVID-19 pandemic. The main predictors of a prolonged LOS were unrelated to the patient's individual health and concerned administrative and organizational factors.

Periprosthetic Infections: Five Steps to Keep Moving Forward in 2021.

The 2010 to 2020 decade produced remarkable advances in the understanding of periprosthetic joint infections (PJI). However, the challenges that surgeons and clinical researchers will face in the coming decade are many. Among the tactics that should comprise the strategy to keep moving forward are: (1) The incorporation of value-based healthcare concepts in PJI diagnosis. (2) Personalized approaches to estimate PJI risk and prevent it. (3) The concentration of PJI cases in multidisciplinary superspecialized units of treatment. (4) The use of a uniform definition to categorize failed and successful episodes of treatment. (5) Using the best available evidence as a benchmark to guide daily bedside decision-making.

Inter- and intra-observer agreement using the new AOSpine sacral fracture classification, with a comparison between spine and pelvic trauma surgeons.

BACKGROUND: Sacral fractures treatment frequently involves both spine and pelvic trauma surgeons; therefore, a consistent communication among surgical specialists is required. We independently assessed the new AOSpine sacral fracture classification's agreement from the perspective of spine and pelvic trauma surgeons. METHODS: Complete computerized tomography (CT) scans of 80 patients with sacral fractures were selected and classified using the new AOSpine sacral classification system by six spine surgeons and three pelvic trauma surgeons. After four weeks, the 80 cases were presented and reassessed by the same raters in a new random sequence. The Kappa coefficient (κ) was used to measure the inter-and intra-observer agreement. RESULTS: The inter-observer agreement considering the fracture severity types (A, B, or C) was substantial for spine surgeons (κ= 0.68 [0.63 - 0.72]) and pelvic trauma surgeons (κ= 0.74 (0.64 - 0.84). Regarding the subtypes, both groups achieved moderate agreement with κ= 0.52 (0.49 - 0.54) for spine surgeons and κ= 0.51 (0.45 - 0.57) for pelvic trauma surgeons. The intra-observer agreement considering the fracture types was substantial for spine surgeons (κ= 0.74 [0.63 - 0.75]) and almost perfect for pelvic trauma surgeons (κ= 0.84 [0.74 - 0.93]). Concerning the subtypes, both groups achieved substantial agreement with, κ= 0.61 (0.56 - 0.67) for spine surgeons and κ= 0.68 (0.62 - 0.74) for pelvic trauma surgeons. CONCLUSION: This classification allows an adequate communication for spine surgeons and pelvic trauma surgeons at the fracture severity type, but the agreement is only moderate at the subtype level. Future prospective studies are required to evaluate whether this classification allows for treatment recommendations and establishing prognosis in patients with sacral fractures.

Premio de Investigación SCHOT 2020: desarrollo y validación de un modelo multivariables de predicción de estadía hospitalaria en pacientes mayores de 65 años sometidos artroplastia total de cadera electiva en Chile utilizando aprendizaje de máquinas / 2020 SCHOT Research award: Development an a multivariable prediction model of hospital stay in elderly chilean patients undergoing elective total hip arthroplasty using machine learning

INTRODUCCIÓN La predicción de la estadía hospitalaria luego de una artroplastia total de cadera (ATC) electiva es crucial en la evaluación perioperatoria de los pacientes, con un rol determinante desde el punto de vista operacional y económico. Internacionalmente, se han empleado macrodatos (big data, en inglés) e inteligencia artificial para llevar a cabo evaluaciones pronósticas de este tipo. El objetivo del presente estudio es desarrollar y validar, con el empleo del aprendizaje de máquinas (machine learning, en inglés), una herramienta capaz de predecir la estadía hospitalaria de pacientes chilenos mayores de 65 años sometidos a ATC por artrosis. MATERIALES Y MÉTODOS Empleando los registros electrónicos de egresos hospitalarios anonimizados del Departamento de Estadísticas e Información de Salud (DEIS), se obtuvieron los datos de 8.970 egresos hospitalarios de pacientes sometidos a ATC por artrosis entre los años 2016 y 2018. En total, 15 variables disponibles en el DEIS, además del porcentaje de pobreza de la comuna de origen del paciente, fueron incluidos para predecir la probabilidad de que un paciente presentara una estadía acortada (< 3 días) o prolongada (> 3 días) luego de la cirugía. Utilizando técnicas de aprendizaje de máquinas, 8 algoritmos de predicción fueron entrenados con el 80% de la muestra. El 20% restante se empleó para validar las capacidades predictivas de los modelos creados a partir de los algoritmos. La métrica de optimización se evaluó y ordenó en un ranking utilizando el área bajo la curva de característica operativa del receptor (area under the receiver operating characteristic curve, AUC-ROC, en inglés), que corresponde a cuan bien un modelo puede distinguir entre dos grupos. RESULTADOS El algoritmo XGBoost obtuvo el mejor desempeño, con una AUC-ROC promedio de 0,86 (desviación estándar [DE]: 0,0087). En segundo lugar, observamos que el algoritmo lineal de máquina de vector de soporte (support vector machine, SVM, en inglés) obtuvo una AUC-ROC de 0,85 (DE: 0,0086). La importancia relativa de las variables explicativas demostró que la región de residencia, el servicio de salud, el establecimiento de salud donde se operó el paciente, y la modalidad de atención son las variables que más determinan el tiempo de estadía de un paciente. DISCUSIÓN El presente estudio desarrolló algoritmos de aprendizaje de máquinas basados en macrodatos chilenos de libre acceso, y logró desarrollar y validar una herramienta que demuestra una adecuada capacidad discriminatoria para predecir la probabilidad de estadía hospitalaria acortada versus prolongada en adultos mayores sometidos a ATC por artrosis. CONCLUSIÓN Los algoritmos creados a traves del empleo del aprendizaje de máquinas permiten predecir la estadía hospitalaria en pacientes chilenos operado de artroplastia total de cadera electiva

Introduction The prediction of the length of hospital stay after elective total hip arthroplasty (THA) is crucial in the perioperative evaluation of the patients, and it plays a decisive role from the operational and economic point of view. Internationally, big data and artificial intelligence have been used to perform prognostic evaluations of this type. The present study aims to develop and validate, through the use of artificial intelligence (machine learning), a tool capable of predicting the hospital stay of patients over 65 years of age undergoing THA for osteoarthritis. Material and Methods Using the electronic records of hospital discharges de-identified from the Department of Health Statistics and Information (Departamento de Estadísticas e Información de Salud, DEIS, in Spanish), the data of 8,970 hospital discharges of patients who had undergone THA for osteoarthritis between 2016 and 2018 were obtained. A total of 15 variables available in the DEIS registry, in addition to the poverty rate in the patient's borough of origin were included to predict the probability that a patient would have a shortened (< 3 days) or prolonged (> 3 days) stay after surgery. By using machine learning techniques, 8 prediction algorithms were trained with 80% of the sample. The remaining 20% was used to validate the predictive capabilities of the models created from the algorithms. The optimization metric was evaluated and ranked using the area under the receiver operating characteristic curve (AUC-ROC), which corresponds to how well a model can distinguish between two groups. Results The XGBoost algorithm had the best performance, with an average AUC-ROC of 0.86 (standard deviation [SD]: 0.0087). Secondly, we observed that the linear support vector machine (SVM) algorithm obtained an AUC-ROC of 0.85 (SD: 0.0086). The relative importance of the explanatory variables showed that the region of residence, the administrative health service, the hospital where the patient was operated on, and the care modality are the variables that most determine the length of stay. Discussion The present study developed machine learning algorithms based on freeaccess Chilean big data, which helped create and validate a tool that demonstrates an adequate discriminatory capacity to predict shortened versus prolonged hospital stay in elderly patients undergoing elective THA. Conclusion The algorithms created through the use of machine learning allow to predict the hospital stay in Chilean patients undergoing elective total hip arthroplasty Introduction The prediction of the length of hospital stay after elective total hip arthroplasty (THA) is crucial in the perioperative evaluation of the patients, and it plays a decisive role from the operational and economic point of view. Internationally, big data and artificial intelligence have been used to perform prognostic evaluations of this type. The present study aims to develop and validate, through the use of artificial intelligence (machine learning), a tool capable of predicting the hospital stay of patients over 65 years of age undergoing THA for osteoarthritis. Material and Methods Using the electronic records of hospital discharges de-identified from the Department of Health Statistics and Information (Departamento de Estadísticas e Información de Salud, DEIS, in Spanish), the data of 8,970 hospital discharges of patients who had undergone THA for osteoarthritis between 2016 and 2018 were obtained. A total of 15 variables available in the DEIS registry, in addition to the poverty rate in the patient's borough of origin were included to predict the probability that a patient would have a shortened (< 3 days) or prolonged (> 3 days) stay after surgery. By using machine learning techniques, 8 prediction algorithms were trained with 80% of the sample. The remaining 20% was used to validate the predictive capabilities of the models created from the algorithms. The optimization metric was evaluated and ranked using the area under the receiver operating characteristic curve (AUC-ROC), which corresponds to how well a model can distinguish between two groups. Results The XGBoost algorithm had the best performance, with an average AUC-ROC of 0.86 (standard deviation [SD]: 0.0087). Secondly, we observed that the linear

Protrusio acetabular en artroplastía total de cadera: diez tácticas para un buen resultado quirúrgico / Total hip arthroplasty in protrusio acetabuli: ten tips to improve surgical outcomes

Los pacientes candidatos a artroplastía total de cadera con protrusio acetabular asociada generan distintos desafíos en los equipos quirúrgicos. Múltiples estrategias han sido utilizadas a lo largo de los años para optimizar los resultados. Mediante una revisión de la evidencia actualizada disponible, proponemos diez tácticas a realizar en el manejo de estos pacientes que pueden mejorar y hacer predecible el tratamiento de un paciente con protrusio acetabular al que se le realiza una artroplastía total de cadera. Nivel de Evidencia V.

Patients with acetabular protrusio and osteoarthritis are a challenge for the surgical team. Many strategies have been developed to anticipate, plan and optimize the surgical results of these patients. Based on the current available clinical evidence, we propose ten tips to improve the surgical management of hip arthroplasty patients with protrusio acetabuli. Level of Evidence V.

Preparation for the next COVID-19 wave: The European Hip Society and European Knee Associates recommendations.

PURPOSE: To plan for the continuance of elective hip and knee arthroplasty during a resurgence or new wave of COVID-19 infections. METHOD: A systematic review was conducted using the terms "COVID-19" or "SARS-Cov-2" and "second wave". No relevant citations were found to inform on recommendations the plan. Therefore, an expert panel of the European Hip Society and the European Knee Associates was formed to provide the recommendations. RESULTS: Overall, the recommendations consider three phases; review of the first wave, preparation for the next wave, and during the next wave. International and national policies will drive most of the management. The recommendations focus on the preparation phase and, in particular, the actions that the individual surgeon needs to undertake to continue with, and practice, elective arthroplasty during the next wave, as well as planning their personal and their family's lives. The recommendations expect rigorous data collection during the next wave, so that a cycle of continuous improvement is created to take account of any future waves. CONCLUSIONS: The recommendations for planning to continue elective hip and knee arthroplasty during a new phase of the SARS-Cov-2 pandemic provide a framework to reduce the risk of a complete shutdown of elective surgery. This involves engaging with hospital managers and other specialities in the planning process. Individuals have responsibilities to themselves, their colleagues, and their families, beyond the actual delivery of elective arthroplasty.

Hip fractures in the elderly Chilean population: a projection for 2030.

Using national databases, we projected the magnitude of hip fractures among the elderly Chilean population by 2030. Hip fractures will increase by 27.5% from 2018 to 9862 cases (95% CI = 8760 to 10,965). The most substantial growth is expected to occur in patients older than 80 years of age. INTRODUCTION: There is scarce information regarding the magnitude of hip fractures that will be observed in Latin American countries in the near future. The aim of this study is to project the volume of hip fractures in the elderly Chilean population by the year 2030. METHODS: The database of the Chilean Department of Statistics and Health Information, which includes all hospital discharges within Chile, was employed to evaluate the volume of hip fractures observed from 2012 to 2018 in the elderly population (≥ 65 years old). The annual incidence was determined by combining the volume of fractures with census data. Linear regression analysis was performed to determine the projected volume of hip fractures for 2030. RESULTS: For 2030, hip fractures among the elderly population are projected to grow 27.5% (R2 = 0.92) to 9862 cases (95% CI = 8760 to 10,965). In the group from 65 to 79 years old, hip fractures are expected to grow 21.6% (R2 = 0.87) to 3046 cases (95% CI = 2686 to 3405). In patients ≥ 80 years of age, hip fractures are expected to grow 30.1% (R2 = 0.9) to 6817 cases (95% CI = 5889 to 7744). CONCLUSIONS: A substantial growth in the volume of hip fractures is expected for the next decade among the elderly Chilean population, especially in patients ≥ 80 years of age. A national healthcare strategy should consider efforts to mitigate the impact of the future burden related to patients' care.

Diagnosis of Periprosthetic Joint Infection: The Role of Nuclear Medicine May Be Overestimated.

Although the International Consensus Meeting on Periprosthetic Joint Infection's definition of periprosthetic joint infection (PJI) does not include nuclear imaging as part of the diagnostic criteria, many contemporary nuclear imaging studies are reporting exceptional results in PJI diagnosis. We conducted a systematic review of studies published from 2004 to 2012 reporting the accuracy of nuclear imaging for diagnosis of PJI, utilizing a specially designed tool (QUADAS-2) for critical appraisal and investigation of bias. Our results revealed high risk of bias as well as high levels of concern regarding the clinical applicability of these tests in a majority of the studies. On the basis of our findings, we recommend that the use of nuclear imaging for diagnosis of PJI be limited to a few select cases.

Hip arthroscopy and osteoarthritis: Where are the limits and indications?

The use of hip arthroscopy, as a surgical technique, has increased significantly over the past ten years. The procedure has shown good and excellent results in symptom relief and function improvement for patients with femoro-acetabular impingement (FAI) and concurrent chondro-labral lesions. It is also a reliable method to correct the characteristic pathomorphologic alteration of FAI. However, surgical results are less successful among patients with advanced articular damage and secondary hip osteoarthritis. The aim of this article is to present some clinical and imagenological tools to discriminate the good candidates for arthroscopic FAI treatment from those who are not, due to extensive articular damage.

Does the nature of chondrolabral injury affect the results of open surgery for femoroacetabular impingement?

BACKGROUND: The degree to which patient characteristics, clinical outcomes, and the nature, severity, and corresponding treatment of chondrolabral injury in femoroacetabular impingement (FAI) is associated with failure after surgery is incompletely understood. QUESTIONS/PURPOSES: (1) Are patient factors associated with failure (age, sex, body mass index, and preoperative modified Harris hip score [mHHS]) in the open surgical treatment of FAI? (2) Is the nature of chondrolabral injury associated with failure? (3) Are any specific chondrolabral injury treatment methods superior? METHODS: Between 2000 and 2008, 172 open surgical procedures in 167 patients were performed for the treatment of FAI by two surgeons at two separate academic medical centers. Ultimately, 142 patients were included in this retrospective study. Mean followup was 3 years (range, 1-12 years). Patient and clinical factors along with the nature, severity, and treatment of chondrolabral injuries were assessed for an association with failure, defined as conversion to THA or the inability to achieve the minimum clinically important difference of the mHHS. Thirty-two percent (45 of 142) of patients failed open surgical treatment of FAI. RESULTS: Patient factors associated with failure included age (odds ratio [OR], 1.04; p = 0.036) and preoperative mHHS (OR, 4.42; p = 0.033). Neither the nature of the labral lesion nor the severity of the chondral lesion demonstrated a relationship with failure (p > 0.05). Surgically, labral refixation was associated with a decrease in the risk of failure (OR, 0.31; p = 0.039). CONCLUSIONS: We were unable to identify an increased risk of poor outcomes based on sex, body mass index, or severity of chondrolabral lesions. We did find an increased risk of poorer outcomes associated with age. Labral refixation was associated with a decrease in the risk of failure indicating that treatment methodology, rather than the nature of the chondrolabral injury, may be associated with clinical failure. Future studies will be needed to help determine optimal treatment strategies for chondrolabral injuries.

"Warning signs" of primary immunodeficiency among patients with periprosthetic joint infection.

PURPOSE: The use of ten clinical "warning signs" has been suggested as a screening tool to identify patients that may have primary immunodeficiency (PID) conditions in adulthood. This study aimed to evaluate the presence of these "warning signs" among a cohort of patients with periprosthetic joint infections (PJI), in order to detect those cases that may have had a PID contributing to development of infection. METHODS: A descriptive study using our institutional PJI database was conducted. 185 patients with more than 15 medical consultations in our healthcare network before the diagnosis of PJI were considered eligible for the study. The presence of the "warning signs" was retrospectively evaluated using medical records. RESULTS: Twenty-seven patients (14.5%) presented with one or more "warning signs" of PID; however, 24 of the individuals had another immunocompromising condition and were thought to suffer secondary immunodeficiency. Among the remaining 3 patients, PID was confirmed in 1 individual who was found to have hypogammaglobulinemia. CONCLUSIONS: It appears that some patients who develop PJI could suffer from a primary immunodeficiency status that may be detected using "warning signs" questionnaire. The administration of these questions to patients with multiple infections may lead to identification of a primary immunodeficiency status which may in turn influence the outcome of elective arthroplasty or PJI, when develops.

Direct anterior approach for total hip arthroplasty: indications, technique, and results.

The direct anterior approach (DAA) to the hip was initially described in the 19th century and has been used sporadically for total hip arthroplasty (THA). In the past decade, enthusiasm for the approach has been renewed because of increased demand for minimally invasive techniques. New surgical instruments and tables designed specifically for use with the DAA for THA have made the approach more accessible to surgeons. Some authors claim that this approach results in less muscle damage and pain as well as rapid recovery, although limited data exist to support these claims. The DAA may be comparable to other THA approaches, but there is no evidence to date that shows improved long-term outcomes for patients. The steep learning curve and complications unique to this approach (fractures and nerve damage) have been well described. However, the incidence of these complications decreases with greater surgeon experience. A question of keen interest to hip surgeons and patients is whether the DAA results in improved early outcomes and long-term results comparable to those of other approaches for THA.

Diagnosis of periprosthetic joint infection in Medicare patients: multicriteria decision analysis.

BACKGROUND: In the setting of finite healthcare resources, developing cost-efficient strategies for periprosthetic joint infection (PJI) diagnosis is paramount. The current levels of knowledge allow for PJI diagnostic recommendations based on scientific evidence but do not consider the benefits, opportunities, costs, and risks of the different diagnostic alternatives. QUESTIONS/PURPOSES: We determined the best diagnostic strategy for knee and hip PJI in the ambulatory setting for Medicare patients, utilizing benefits, opportunities, costs, and risks evaluation through multicriteria decision analysis (MCDA). METHODS: The PJI diagnostic definition supported by the Musculoskeletal Infection Society was employed for the MCDA. Using a preclinical model, we evaluated three diagnostic strategies that can be conducted in a Medicare patient seen in the outpatient clinical setting complaining of a painful TKA or THA. Strategies were (1) screening with serum markers (erythrocyte sedimentation rate [ESR]/C-reactive protein [CRP]) followed by arthrocentesis in positive cases, (2) immediate arthrocentesis, and (3) serum markers requested simultaneously with arthrocentesis. MCDA was conducted through the analytic hierarchy process, comparing the diagnostic strategies in terms of benefits, opportunities, costs, and risks. RESULTS: Strategy 1 was the best alternative to diagnose knee PJI among Medicare patients (normalized value: 0.490), followed by Strategy 3 (normalized value: 0.403) and then Strategy 2 (normalized value: 0.106). The same ranking of alternatives was observed for the hip PJI model (normalized value: 0.487, 0.405, and 0.107, respectively). The sensitivity analysis found this sequence to be robust with respect to benefits, opportunities, and risks. However, if during the decision-making process, cost savings was given a priority of higher than 54%, the ranking for the preferred diagnostic strategy changed. CONCLUSIONS: After considering the benefits, opportunities, costs, and risks of the different available alternatives, our preclinical model supports the American Academy of Orthopaedic Surgeons recommendations regarding the use of serum markers (ESR/CRP) before arthrocentesis as the best diagnostic strategy for PJI among Medicare patients. LEVEL OF EVIDENCE: Level II, economic and decision analysis. See Instructions to Authors for a complete description of levels of evidence.