Expanded analysis for patients with acute cholecystitis indicates outcomes vary based on COVID-19 status and treatment modality.

BACKGROUND: The impact of different phases of COVID-19 infection on outcomes from acute calculous cholecystitis (ACC) is not well understood. Therefore, we examined outcomes of acute cholecystitis during the COVID-19 pandemic, comparing the effect of different treatment modalities and COVID-19 infection status. We hypothesized that patients with acute COVID-19 would have worse outcomes than COVID-negative patients, but there would be no difference between COVID-negative and COVID-recovered patients. METHODS: We used 2020-2023 National COVID Cohort Collaborative data to identify adults with ACC. Treatment (antibiotics-only, cholecystostomy tube, or cholecystectomy) and COVID-19 status (negative, active, or recovered) were collected. Treatment failure of nonoperative managements was noted. Adjusted analysis using a series of generalized linear models controlled for confounders (age, sex, body mass index, Charlson comorbidity index, severity at presentation, and year) to better assess differences in outcomes among treatment groups, as well as between COVID-19 groups. RESULTS: In total, 32,433 patients (skewed count) were included: 29,749 COVID-negative, 2112 COVID-active, and 572 (skewed count) COVID-recovered. COVID-active had higher rates of sepsis at presentation. COVID-negative more often underwent cholecystectomy. Unadjusted, COVID-active had higher 30-day mortality, 30-day complication, and longer length of stay than COVID-negative and COVID-recovered. Adjusted analysis revealed cholecystectomy carried lower odds of mortality for COVID-active and COVID-negative patients than antibiotics or cholecystostomy. COVID-recovered patients' mortality was unaffected by treatment modality. Treatment failure from antibiotics was more common for COVID-negative patients. CONCLUSION: Acute cholecystitis outcomes are affected by phase of COVID-19 infection and treatment modality. Cholecystectomy does not lead to worse outcomes for COVID-active and COVID-recovered patients than nonoperative treatments; thus, these patients can be considered for cholecystectomy if their physiology is not prohibitive.

Survival Tree Provides Individualized Estimates of Survival After Lung Transplant.

INTRODUCTION: Identifying contributors to lung transplant survival is vital in mitigating mortality. To enhance individualized mortality estimation and determine variable interaction, we employed a survival tree algorithm utilizing recipient and donor data. METHODS: United Network Organ Sharing data (2000-2021) were queried for single and double lung transplants in adult patients. Graft survival time <7 d was excluded. Sixty preoperative and immediate postoperative factors were evaluated with stepwise logistic regression on mortality; final model variables were included in survival tree modeling. Data were split into training and testing sets and additionally validated with 10-fold cross validation. Survival tree pruning and model selection was based on Akaike information criteria and log-likelihood values. Estimated survival probabilities and log-rank pairwise comparisons between subgroups were calculated. RESULTS: A total of 27,296 lung transplant patients (8175 single; 19,121 double lung) were included. Stepwise logistic regression yielded 47 significant variables associated with mortality. Survival tree modeling returned six significant factors: recipient age, length of stay from transplant to discharge, recipient ventilator duration post-transplant, double lung transplant, recipient reintubation post-transplant, and donor cytomegalovirus status. Eight subgroups consisting of combinations of these factors were identified with distinct Kaplan-Meier survival curves. CONCLUSIONS: Survival trees provide the ability to understand the effects and interactions of covariates on survival after lung transplantation. Individualized survival probability with this technique found that preoperative and postoperative factors influence survival after lung transplantation. Thus, preoperative patient counseling should acknowledge a degree of uncertainty given the influence of postoperative factors.

Addressing disparities in European cancer outcomes: a qualitative study Protocol of the BEACON project.

Introduction: Health disparities represent a crucial factor in cancer survival rates, awareness, quality of life, and mental health of people receiving a cancer diagnosis and their families. Income, education, geographic location, and ethnicity are some of the most important underlying reasons for health disparities in cancer across Europe. Costs of healthcare, access to information, psycho-oncological support options, integration of cancer research and innovative care, and multidisciplinary cancer teams are the main target areas when it comes to addressing disparities in the cancer context. As part of the Beacon Project (BEACON), we developed a protocol for a qualitative study to explore and identify any relevant reasons for cancer inequalities and disparities in Europe. Methods: Our four stakeholders namely, cancer patients, healthcare providers, researchers, and policymakers will be recruited online, facilitated by collaborative efforts with cancer organizations from various European countries, including but not limited to Italy, Croatia, Estonia, and Slovenia. Qualitative online focus group discussions for each stakeholder will be conducted and transcribed. Subsequently, thematic analysis will be used to identify reasons and aspects that may contribute to the existing disparities in cancer outcomes at various levels of engagement and from different stakeholders' perspectives. Results from focus groups will inform a subsequent Delphi study and a SWOT analysis methodology. Discussion: Although advances in medical research, cancer screening and treatment options are constantly progressing, disparities in access to and awareness of healthcare in cancer patients are even more noticeable. Thus, mapping the capacity and capability of cancer centres in the European Union, creating decision support tools that will assist the four stakeholders' information needs and improving the quality of European cancer centres will be the main objectives of the BEACON project. The current protocol will outline the methodological and practical procedures to conduct online focus group discussions with different stakeholders.

Correction: A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial.

[This corrects the article DOI: 10.2196/48852.].

Is nonoperative management of appendicitis inferior in the COVID-19 era?

BACKGROUND: Previous studies on nonoperative management (NOM) of acute appendicitis (AA) indicated comparable outcomes to surgery, but the effect of COVID-19 infection on appendicitis outcomes remains unknown. Thus, we evaluate appendicitis outcomes during the COVID-19 pandemic to determine the effect of COVID-19 infection status and treatment modality. We hypothesized that active COVID-19 patients would have worse outcomes than COVID-negative patients, but that outcomes would not differ between recovered COVID-19 and COVID-negative patients. Moreover, we hypothesized that outcomes would not differ between nonoperative and operative management groups, regardless of COVID-19 status. METHODS: We queried the National COVID Cohort Collaborative from 2020 to 2023 to identify adults with AA who underwent operative or NOM. COVID-19 status was denoted as follows: COVID-negative, COVID-active, or COVID-recovered. Intention to treat was used for NOM. Propensity score-balanced analysis was performed to compare outcomes within COVID groups, as well as within treatment modalities. RESULTS: A total of 37,868 patients were included: 34,866 COVID-negative, 2,540 COVID-active, and 460 COVID-recovered. COVID-active and recovered less often underwent operative management. Unadjusted, there was no difference in mortality between COVID groups for operative management. There was no difference in rate of failure of NOM between COVID groups. Adjusted analysis indicated, compared with operative, NOM carried higher odds of mortality and readmission for COVID-negative and COVID-active patients. CONCLUSION: This study demonstrates higher odds of mortality among NOM of appendicitis and near equivalent outcomes for operative management regardless of COVID-19 status. We conclude that NOM of appendicitis is associated with worse outcomes for COVID-active and COVID-negative patients. In addition, we conclude that a positive COVID test or recent COVID-19 illness alone should not preclude a patient from appendectomy for AA. Surgeon clinical judgment of a patient's physiology and surgical risk should, of course, inform the decision to proceed to the operating room. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level III.

Artificial Intelligence in Surgical Research: Accomplishments and Future Directions.

MINI-ABSTRACT: The study introduces various methods of performing conventional ML and their implementation in surgical areas, and the need to move beyond these traditional approaches given the advent of big data. OBJECTIVE: Investigate current understanding and future directions of machine learning applications, such as risk stratification, clinical data analytics, and decision support, in surgical practice. SUMMARY BACKGROUND DATA: The advent of the electronic health record, near unlimited computing, and open-source computational packages have created an environment for applying artificial intelligence, machine learning, and predictive analytic techniques to healthcare. The "hype" phase has passed, and algorithmic approaches are being developed for surgery patients through all stages of care, involving preoperative, intraoperative, and postoperative components. Surgeons must understand and critically evaluate the strengths and weaknesses of these methodologies. METHODS: The current body of AI literature was reviewed, emphasizing on contemporary approaches important in the surgical realm. RESULTS AND CONCLUSIONS: The unrealized impacts of AI on clinical surgery and its subspecialties are immense. As this technology continues to pervade surgical literature and clinical applications, knowledge of its inner workings and shortcomings is paramount in determining its appropriate implementation.

A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial.

BACKGROUND: Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant. OBJECTIVE: The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patients' adherence behavior. METHODS: This prospective, randomized controlled study is nested in a large-scale international project named "Enhancing therapy adherence among metastatic breast cancer patients" (Pfizer 65080791), aimed to develop a predictive model of nonadherence and associated DSS and guidelines to foster patients' engagement and therapy adherence. A web-based DSS named TREAT (treatment adherence support) was developed using a patient-driven approach, with 4 sections, that is, Section A: Metastatic Breast Cancer; Section B: Adherence to Cancer Therapies; Section C: Promoting Adherence; and Section D: My Adherence Diary. Moreover, a machine learning-based web application was developed to predict patients' risk factors of adherence to anticancer treatment, specifically pertaining to physical status and comorbid conditions, as well as short and long-term side effects. Overall, 100 patients consecutively admitted at the European Institute of Oncology (IEO) at the Division of Medical Senology will be enrolled; 50 patients with metastatic breast cancer will be exposed to the DSS and machine learning web application for 3 months (experimental group), and 50 patients will not be exposed to the intervention (control group). Each participant will fill a weekly medication diary and a set of standardized self-reports evaluating psychological and quality of life variables (Adherence Attitude Inventory, Beck Depression Inventory-II, Brief Pain Inventory, 13-item Sense of Coherence scale, Brief Italian version of Cancer Behavior Inventory, European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, 8-item Morisky Medication Adherence Scale, State-Trait Anxiety Inventory forms I and II, Big Five Inventory, and visual analogue scales evaluating risk perception). The 3 assessment time points are T0 (baseline), T1 (1 month), T2 (2 months), and T3 (3 months). This study was approved by the IEO ethics committee (R1786/22-IEO 1907). RESULTS: The recruitment process started in May 2023 and is expected to conclude on December 2023. CONCLUSIONS: The contribution of machine learning techniques through risk-predictive models integrated into DSS will enable medication adherence by patients with cancer. TRIAL REGISTRATION: ClinicalTrials.gov NCT06161181; https://clinicaltrials.gov/study/NCT06161181. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48852.

Interpretable machine learning accurately reclassifies lobectomy surgical approaches by cost.

BACKGROUND: The volume of robotic lung resection continues to increase despite its higher costs and unproven superiority to video-assisted thoracoscopic surgery. We evaluated whether machine learning can accurately identify factors influencing cost and reclassify high-cost operative approaches into lower-cost alternatives. METHODS: The Florida Agency for Healthcare Administration and Centers for Medicare and Medicaid Services Hospital and Physician Compare datasets were queried for patients undergoing open, video-assisted thoracoscopic surgery and robotic lobectomy. K-means cluster analysis was used to identify robotic clusters based on total cost. Predictive models were built using artificial neural networks, Support Vector Machines, Classification and Regression Trees, and Gradient Boosted Machines algorithms. Models were applied to the high-volume robotic group to determine patients whose cost cluster changed if undergoing a video-assisted thoracoscopic surgery approach. A local interpretable model-agnostic explanation approach personalized cost per patient. RESULTS: Of the 6,618 cases included in the analysis, we identified 4 cost clusters. Application of artificial neural networks to the robotic subgroup identified 1,642 (65%) cases with no re-assignment of cost cluster, 583 (23%) with reduced costs, and 300 (12%) with increased costs if they had undergone video-assisted thoracoscopic surgery approach. The 5 overall highest cost predictors were patient admission from the clinic, diagnosis of metastatic cancer, presence of cancer, urgent hospital admission, and dementia. CONCLUSION: K-means cluster analysis and machine learning identify a patient population that may undergo video-assisted thoracoscopic surgery or robotic lobectomy without a significant difference in total cost. Local interpretable model-agnostic explanation identifies individual patient factors contributing to cost. Application of this modeling may reliably stratify high-cost patients into lower-cost approaches and provide a rationale for reducing expenditure.

"Interventions to increase adherence to oral therapies in breast cancer patients: A systematic review based on the behavior change technique taxonomy".

OBJECTIVE: High rates of non-adherence to oral medications in breast cancer (BC) patients have been reported. Here we provide an up-to-date systematic review of the interventions aimed at increasing adherence to oral medication in BC patients, with a particular focus on the content of the interventions. METHODS: PubMed, Scopus, Embase and Ovid databases and reference lists of relevant studies were searched through October 2022. Studies which (1) described an intervention aimed at increasing adherence to oral anticancer medication, (2) included (or planned to include) at least one sub-group of BC patients, (3) were written in English, and (4) with full-text available were included. The contents of the interventions were coded using the Behavior Change Technique Taxonomy. Quality assessment was conducted using Downs and Black scale. RESULTS: Thirty-six studies met the inclusion criteria and involved a total sample of 28,528 BC patients. Interventions were mainly delivered with eHealth devices (n = 21) and most of them used mobile app. Other studies used in-person modalities (e.g., CBT, relaxation technique) or written materials (e.g., psycho-educational booklet). The behavior change techniques most frequently implemented were "problem solving," "social support," "information about health consequences," and "prompts/cues". Quality assessment revealed that the higher risk of bias refers to the selection process. CONCLUSIONS: The use of reminders, monitoring patients' medication-taking behaviors and giving feedback were the most frequently implemented techniques in those interventions that resulted significant. If these preliminary observations were to be confirmed by future comparative studies, they should be taken into account when developing new interventions.

Recipient Survival after Orthotopic Liver Transplantation: Interpretable Machine Learning Survival Tree Algorithm for Patient-Specific Outcomes.

BACKGROUND: Elucidating contributors affecting liver transplant survival is paramount. Current methods offer crude global group outcomes. To refine patient-specific mortality probability estimation and to determine covariate interaction using recipient and donor data, we generated a survival tree algorithm, Recipient Survival After Orthotopic Liver Transplantation (ReSOLT), using United Network Organ Sharing (UNOS) transplant data. STUDY DESIGN: The UNOS database was queried for liver transplants in patients ≥18 years old between 2000 and 2021. Preoperative factors were evaluated with stepwise logistic regression; 43 significant factors were used in survival tree modeling. Graft survival of <7 days was excluded. The data were split into training and testing sets and further validated with 10-fold cross-validation. Survival tree pruning and model selection was achieved based on Akaike information criterion and log-likelihood values. Log-rank pairwise comparisons between subgroups and estimated survival probabilities were calculated. RESULTS: A total of 122,134 liver transplant patients were included for modeling. Multivariable logistic regression (area under the curve = 0.742, F1 = 0.822) and survival tree modeling returned 8 significant recipient survival factors: recipient age, donor age, recipient primary payment, recipient hepatitis C status, recipient diabetes, recipient functional status at registration and at transplantation, and deceased donor pulmonary infection. Twenty subgroups consisting of combinations of these factors were identified with distinct Kaplan-Meier survival curves (p < 0.001 among all by log rank test) with 5- and 10-year survival probabilities. CONCLUSIONS: Survival trees are a flexible and effective approach to understand the effects and interactions of covariates on survival. Individualized survival probability following liver transplant is possible with ReSOLT, allowing for more coherent patient and family counseling and prediction of patient outcome using both recipient and donor factors.

Nonelective coronary artery bypass graft outcomes are adversely impacted by Coronavirus disease 2019 infection, but not altered processes of care: A National COVID Cohort Collaborative and National Surgery Quality Improvement Program analysis.

Objective: The effects of Coronavirus disease 2019 (COVID-19) infection and altered processes of care on nonelective coronary artery bypass grafting (CABG) outcomes remain unknown. We hypothesized that patients with COVID-19 infection would have longer hospital lengths of stay and greater mortality compared with COVID-negative patients, but that these outcomes would not differ between COVID-negative and pre-COVID controls. Methods: The National COVID Cohort Collaborative 2020-2022 was queried for adult patients undergoing CABG. Patients were divided into COVID-negative, COVID-active, and COVID-convalescent groups. Pre-COVID control patients were drawn from the National Surgical Quality Improvement Program database. Adjusted analysis of the 3 COVID groups was performed via generalized linear models. Results: A total of 17,293 patients underwent nonelective CABG, including 16,252 COVID-negative, 127 COVID-active, 367 COVID-convalescent, and 2254 pre-COVID patients. Compared to pre-COVID patients, COVID-negative patients had no difference in mortality, whereas COVID-active patients experienced increased mortality. Mortality and pneumonia were higher in COVID-active patients compared to COVID-negative and COVID-convalescent patients. Adjusted analysis demonstrated that COVID-active patients had higher in-hospital mortality, 30- and 90-day mortality, and pneumonia compared to COVID-negative patients. COVID-convalescent patients had a shorter length of stay but a higher rate of renal impairment. Conclusions: Traditional care processes were altered during the COVID-19 pandemic. Our data show that nonelective CABG in patients with active COVID-19 is associated with significantly increased rates of mortality and pneumonia. The equivalent mortality in COVID-negative and pre-COVID patients suggests that pandemic-associated changes in processes of care did not impact CABG outcomes. Additional research into optimal timing of CABG after COVID infection is warranted.

A machine learning approach to high-risk cardiac surgery risk scoring.

INTRODUCTION: In patients undergoing high-risk cardiac surgery, the uncertainty of outcome may complicate the decision process to intervene. To augment decision-making, a machine learning approach was used to determine weighted personalized factors contributing to mortality. METHODS: American College of Surgeons National Surgical Quality Improvement Program was queried for cardiac surgery patients with predicted mortality ≥10% between 2012 and 2019. Multiple machine learning models were investigated, with significant predictors ultimately used in gradient boosting machine (GBM) modeling. GBM-trained data were then used for local interpretable model-agnostic explanations (LIME) modeling to provide individual patient-specific mortality prediction. RESULTS: A total of 194 patient deaths among 1291 high-risk cardiac surgeries were included. GBM performance was superior to other model approaches. The top five factors contributing to mortality in LIME modeling were preoperative dialysis, emergent cases, Hispanic ethnicity, steroid use, and ventilator dependence. LIME results individualized patient factors with model probability and explanation of fit. CONCLUSIONS: The application of machine learning techniques provides individualized predicted mortality and identifies contributing factors in high-risk cardiac surgery. Employment of this modeling to the Society of Thoracic Surgeons database may provide individualized risk factors contributing to mortality.

Machine Learning Refinement of the NSQIP Risk Calculator: Who Survives the "Hail Mary" Case?

BACKGROUND: The American College of Surgeons (ACS) NSQIP risk calculator helps guide operative decision making. In patients with significant surgical risk, it may be unclear whether to proceed with "Hail Mary"-type interventions. To refine predictions, a local interpretable model-agnostic explanations machine (LIME) learning algorithm was explored to determine weighted patient-specific factors' contribution to mortality. STUDY DESIGN: The ACS-NSQIP database was queried for all surgical patients with mortality probability greater than 50% between 2012 and 2019. Preoperative factors (n = 38) were evaluated using stepwise logistic regression; 26 significant factors were used in gradient boosted machine (GBM) modeling. Data were divided into training and testing sets, and model performance was substantiated with 10-fold cross validation. LIME provided individual subject mortality. The GBM-trained model was interpolated to LIME, and predictions were made using the test dataset. RESULTS: There were 6,483 deaths (53%) among 12,248 admissions. GBM modeling displayed good performance (area under the curve = 0.65, 95% CI 0.636-0.671). The top 5 factors (% contribution) to mortality included: septic shock (27%), elevated International Normalized Ratio (22%), ventilator-dependence (14%), thrombocytopenia (14%), and elevated serum creatinine (5%). LIME modeling subset personalized patients by factors and weights on survival. In the entire cohort, mortality positive predictive value with 2 factor combinations was 53.5% (specificity 0.713), 3 combinations 64.2% (specificity 0.835), 4 combinations 72.1% (specificity 0.943), and all 5 combinations 77.9% (specificity 0.993). Conversely, mortality positive predictive value fell to 34% in the absence of 4 factors. CONCLUSIONS: Through the application of machine learning algorithms (GBM and LIME), our model individualized predicted mortality and contributing factors with substantial ACS-NSQIP predicted mortality. USE of machine learning techniques may better inform operative decisions and family conversations in cases of significant surgical risk.

The Application of a Hospital Medical Surge Preparedness Index to Assess National Pandemic and Other Mass Casualty Readiness.

EXECUTIVE SUMMARY: This article describes the use and findings of the Hospital Medical Surge Preparedness Index (HMSPI) tool to improve the understanding of hospitals' ability to respond to mass casualty events such as the COVID-19 pandemic. For this investigation, data from the U.S. Census Bureau, the Dartmouth Atlas Project, and the 2005 to 2014 annual surveys of the American Hospital Association (AHA) were analyzed. The HMSPI tool uses variables from the AHA survey and the other two sources to allow facility, county, and referral area index calculations. Using the three data sets, the HMSPI also allows for an index calculation for per capita ratios and by political (state or county) boundaries. In this use case, the results demonstrated increases in county and state HMSPI scores through the period of analysis; however, no statistically significant difference was found in HMSPI scores between 2013 and 2014. The HMSPI builds on the limited scientific foundation of medical surge preparedness and could serve as an objective and standardized measure to assess the nation's medical readiness for crises such as the COVID-19 pandemic and other large-scale emergencies such as mass shootings. Future studies are encouraged to refine the score, assess the validity of the HMSPI, and evaluate its relevance in response to future legislative and executive policies that affect preparedness measures.

Development of a Hospital Medical Surge Preparedness Index using a national hospital survey.

To generate a Hospital Medical Surge Preparedness Index that can be used to evaluate hospitals across the United States in regard to their capacity to handle patient surges during mass casualty events. Data from the American Hospital Association's annual survey, conducted from 2005 to 2014. Our sample comprised 6239 hospitals across all 50 states, with an annual average of 5769 admissions. An extensive review of the American Hospital Association survey was conducted and relevant variables applicable to hospital inpatient services were extracted. Subject matter experts then categorized these items according to the following subdomains of the "Science of Surge" construct: staff, supplies, space, and system. The variables within these categories were then analyzed through exploratory and confirmatory factor analyses, concluding with the evaluation of internal reliability. Based on the combined results, we generated individual (by hospital) scores for each of the four metrics and an overall score. The exploratory factor analysis indicated a clustering of variables consistent with the "Science of Surge" subdomains, and this finding was in agreement with the statistics generated through the confirmatory factor analysis. We also found high internal reliability coefficients, with Cronbach's alpha values for all constructs exceeding 0.9. A novel Hospital Medical Surge Preparedness Index linked to hospital metrics has been developed to assess a health care facility's capacity to manage patients from mass casualty events. This index could be used by hospitals and emergency management planners to assess a facility's readiness to provide care during disasters.

Comparative evaluation of the psychometric properties of orthopedic scales for low back pain / Avaliação comparativa das propriedades psicométricas de escalas ortopédicas para dor lombar / Evaluación comparativa de las propiedades psicométricas de escalas ortopédicas para dolor lumbar

ABSTRACT Objective: To evaluate the reliability, response capacity and validity of four scales for low back pain and correlate these scales with each other and the Self-Administered Comorbidity Questionnaire (SACQ). Methods: We evaluated the psychometric properties of four previously selected scales for low back pain: the Roland-Morris Disability Questionnaire (RMDQ), the Quebec Back Pain Questionnaire (QBPDS), the Waddell Disability Index (WDI) and the Back Pain Functional Scale (BPFS) and Self-Administered Comorbidity Questionnaire (SACQ) comorbidity scale. Exploratory and confirmatory factor analyses were conducted. Reliability and internal consistency were measured by Cronbach's alpha. Validity was measured through correlation of the scales with the Self-Administered Comorbidity Questionnaire comorbidity scale and an analysis of the structural equations between them. Results: The scales showed adequate indicators based on the factor structure and showed Kaiser-Meyer-Olkin values above 0.90. After the exploratory factor analysis, all scales showed fit indicators suited to a factor model, following the same pattern as the original validations. Similarly, they showed good internal consistency (Cronbach's alpha greater than .78). The only scale that showed factor loadings suggesting the exclusion of any item was the Roland-Morris. In terms of validity, the scales showed positive correlation coefficients similar to the Self-Administered Comorbidity Questionnaire and between them. Conclusion: Regarding the scales evaluated, they showed similar indications of reliability and internal consistency, such that we did not find sufficient evidence to indicate one scale over another. Level of Evidence I; Diagnostic studies - Investigation of a diagnostic test.

RESUMO Objetivo: Avaliar a confiabilidade, capacidade de resposta e validade de quatro escalas para dor lombar e correlacionar essas escalas entre si e com a escala Self-Administered Comorbidity Questionnaire (SACQ). Métodos: Foram avaliadas as propriedades psicométricas de quatro escalas para dor lombar previamente selecionadas: Roland-Morris Disability Questionnaire (RMDQ), Quebec Back Pain Disability Scale (QBPDS), Waddell Disability Index (WDI) e Back Pain Functional Scale (BPFS) e a escala de comorbidades Self-Administered Comorbidity Questionnaire. Foram realizadas análises fatoriais exploratória e confirmatória, a confiabilidade e consistência interna foram medidas através de alfa de Cronbach e a validade através da correlação de escalas com a escala de comorbidades Self-Administered Comorbidity Questionnaire e através da análise das equações estruturais entre elas. Resultados: As escalas apresentaram indicadores adequados com base na estrutura fatorial e mostraram valores Kaiser-Meyer-Olkin acima de 0,90. Após a análise fatorial exploratória, todas as escalas apresentaram indicadores de aptidão adequados para um modelo de fator seguindo o mesmo padrão que as validações originais. Do mesmo modo, apresentaram boa consistência interna (alfa de Cronbach superior a 0,78). A única escala que apresentou cargas fatoriais que sugeriam a exclusão de algum item foi a Roland-Morris. Em relação à validade, as escalas apresentaram coeficientes de correlação positiva semelhantes à escala Self-Administered Comorbidity Questionnaire e entre si. Conclusão: Quanto às escalas avaliadas, essas apresentaram indicadores de confiabilidade e consistência interna semelhantes, de modo que não encontramos evidências suficientes para indicar uma escala sobre a outra. Nível de Evidência I; Estudos diagnósticos-Investigação de um exame para diagnóstico.

RESUMEN Objetivo: Evaluar la confiabilidad, capacidad de respuesta y validez de cuatro escalas para dolor lumbar y correlacionar estas escalas entre sí y con la escala Self-Administered Comorbidity Questionnaire (SACQ). Métodos: Fueron evaluadas las propiedades de cuatro escalas para dolor lumbar previamente seleccionadas: Roland-Morris Disability Questionnaire (RMDQ), Quebec Back Disability Scale (QBPDS), Waddell Disability Index (WDI) y Back Pain Functional Scale (BNPFS) y la escala de comorbilidades Self-Administered Comorbidity Questionnaire. Fueron realizados análisis factoriales exploratorio y confirmatorio, fueron medidas la confiabilidad y consistencia interna a través de alfa de Cronbach y la validez a través de la correlación con la escala de comorbilidades Self-Administered Comorbidity Questionnaire, y a través del análisis de las ecuaciones estructurales entre ellas. Resultados: Las escalas presentaron indicadores adecuados con base en la estructura de factorial y mostraron valores Kaiser-Meyer-Olkin por encima de 0,90. Después del análisis factorial exploratorio, todas las escalas presentaron indicadores de aptitud adecuados para un modelo de factor siguiendo el mismo patrón que las validaciones originales. Del mismo modo, presentaron buena consistencia interna (alfa de Cronbach mayor que 0,78). La única escala que presentó cargas factoriales que sugerían la exclusión de algún ítem fue la Roland-Morris. Con relación a la validez, las escalas presentaron coeficientes de correlación positiva similares a la escala Self-Administered Comorbidity Questionnaire y entre sí. Conclusión: Cuanto a las escalas evaluadas, éstas presentaron indicadores de confiabilidad y consistencia interna semejantes, por lo que no encontramos evidencias suficientes para indicar una escala sobre otra. Nivel de evidencia I; Estudios diagnósticos - Investigación de un examen para diagnóstico.

Hip Disability and Osteoarthritis Outcome Score (HOOS): A Cross-Cultural Validation of the Brazilian Portuguese Version Study.

Objective Translated and validated outcome instruments are of great importance, since they can be used for researchers studying different populations with the same problem. The objective of the present study was to translate, culturally adapt and validate the Hip Disability and Osteoarthritis Outcome Score (HOOS) into Brazilian Portuguese. Methods The HOOS was translated from English into Brazilian Portuguese, translated back into English, and submitted to an experts committee. It was administered to 100 patients with hip osteoarthritis. The psychometric evaluation included factor analysis; internal reliability measures, test-retest reliability at 7 days, and construct validity comparison with the Brazilian version of the Graded Chronic Pain Scale (GCPS). Results Factor analyses demonstrated a five-factor solution. The test-retest reliability showed a high degree of internal consistency for the five subscales ( pain and physical difficulties , 0.97 at baseline and 0.93 at 7 days; pain and difficulty sitting, lying down and getting up , 0.93 at baseline and 0.89 at 7 days; difficulty flexing the knee , 0.92 at baseline and 0.83 at 7 days; difficulty walking , 0.88 at baseline and 0.87 at 7 days; quality of life , 0.80 at baseline and 0.35 at 7 days). The construct validity was established during the comparison of the Brazilian version of the GCPS. Conclusions A Brazilian version of the HOOS was developed with adequate reliability and validity. It will facilitate evaluation of the hip within a large patient population and across cultures.

Hip Disability and Osteoarthritis Outcome Score (HOOS): A Cross-Cultural Validation of the Brazilian Portuguese Version Study / Hip disability and osteoarthritis outcome score (HOOS): um estudo de validação intercultural da versão na língua portuguesa

Abstract Objective Translated and validated outcome instruments are of great importance, since they can be used for researchers studying different populations with the same problem. The objective of the present study was to translate, culturally adapt and validate the Hip Disability and Osteoarthritis Outcome Score (HOOS) into Brazilian Portuguese. Methods The HOOS was translated from English into Brazilian Portuguese, translated back into English, and submitted to an experts committee. It was administered to 100 patients with hip osteoarthritis. The psychometric evaluation included factor analysis; internal reliability measures, test-retest reliability at 7 days, and construct validity comparison with the Brazilian version of the Graded Chronic Pain Scale (GCPS). Results Factor analyses demonstrated a five-factor solution. The test-retest reliability showed a high degree of internal consistency for the five subscales (pain and physical difficulties, 0.97 at baseline and 0.93 at 7 days; pain and difficulty sitting, lying down and getting up, 0.93 at baseline and 0.89 at 7 days; difficulty flexing the knee, 0.92 at baseline and 0.83 at 7 days; difficulty walking, 0.88 at baseline and 0.87 at 7 days; quality of life, 0.80 at baseline and 0.35 at 7 days). The construct validity was established during the comparison of the Brazilian version of the GCPS. Conclusions A Brazilian version of the HOOS was developed with adequate reliability and validity. It will facilitate evaluation of the hip within a large patient population and across cultures.

Resumo Objetivo Escalas traduzidas e validadas são de grande importância, pois podem ser utilizadas por pesquisadores que estudam diferentes populações como mesmo problema. O objetivo do presente estudo foi traduzir, adaptar culturalmente e validar a escala Hip Disability and Osteoarthritis Outcome Score (HOOS) para a língua portuguesa. Métodos O HOOS foi traduzido do inglês para a língua portuguesa, traduzido de volta para o inglês e submetido a um comitê de especialistas. Foi administrado a 100 pacientes com osteoartrite de quadril. A avaliação psicométrica incluiu a análise fatorial; medidas de confiabilidade interna, confiabilidade de teste-reteste em 7 dias e a comparação de validade de conteúdo com a versão brasileira da Escala de Dor Crônica Graduada (GCPS, na sigla em inglês). Resultados A análise fatorial demonstrou uma solução de cinco fatores. A confiabilidade de teste-reteste mostrou um alto grau de consistência interna para as 5 subescalas (dor e dificuldades físicas, 0,97 no 1° dia e 0,93 aos 7 dias; dor e dificuldade em sentar, deitar e levantar, 0,93 no 1° dia e 0,89 aos 7 dias; dificuldade em flexionar o joelho, 0,92 no 1° dia e 0,83 aos 7 dias; dificuldade de caminhada, 0,88 no 1° dia e 0,87 aos 7 dias; qualidade de vida, 0,80 no 1° dia e 0,35 aos 7 dias). A validade de conteúdo foi estabelecida durante a comparação da versão brasileira da GCPS. Conclusões Uma versão brasileira do HOOS foi desenvolvida com confiabilidade e validade adequadas. Isso facilitará a avaliação clínica do quadril em uma grande população de pacientes e entre diferentes culturas.

Recommendations for the Prevention of Bisphosphonate-Related Osteonecrosis of the Jaw: A Systematic Review.

OBJECTIVES: The aim of this study was to assess the quality of and outline the differences among recommendations of published clinical practice guidelines (CPGs) for the management of bisphosphonate-associated osteonecrosis of the jaw. METHODS: We conducted a systematic literature search in PubMed, Cochrane, Embase, Web of Science, and Google web site. We selected CPGs supported by a nongovernmental organization or national institutes, related to bisphosphonate-associated osteonecrosis of the jaw in adults, in English language, and dated from January 2008 onward. The validity of each included CPG was appraised according to 2 validated appraisal tools for CPG that were independently used by 2 reviewers. RESULTS: We identified 724 articles, of which 13 were included based on our eligibility criteria. Most CPGs were of good quality based on the appraisal tools for CPGs used in this study. CONCLUSION: We did not find consensus on all the recommendations of the evaluated CPGs. Thus, each clinical case must be assessed individually, considering the risks and benefits on the proposed dental treatment.