Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures.

BACKGROUND: The frequency of hip and knee arthroplasty surgeries has been rising steadily in recent decades. This trend is attributed to an aging population, leading to increased demands on healthcare systems. Fast Track (FT) surgical protocols, perioperative procedures designed to expedite patient recovery and early mobilization, have demonstrated efficacy in reducing hospital stays, convalescence periods, and associated costs. However, the criteria for selecting patients for FT procedures have not fully capitalized on the available patient data, including patient-reported outcome measures (PROMs). METHODS: Our study focused on developing machine learning (ML) models to support decision making in assigning patients to FT procedures, utilizing data from patients' self-reported health status. These models are specifically designed to predict the potential health status improvement in patients initially selected for FT. Our approach focused on techniques inspired by the concept of controllable AI. This includes eXplainable AI (XAI), which aims to make the model's recommendations comprehensible to clinicians, and cautious prediction, a method used to alert clinicians about potential control losses, thereby enhancing the models' trustworthiness and reliability. RESULTS: Our models were trained and tested using a dataset comprising 899 records from individual patients admitted to the FT program at IRCCS Ospedale Galeazzi-Sant'Ambrogio. After training and selecting hyper-parameters, the models were assessed using a separate internal test set. The interpretable models demonstrated performance on par or even better than the most effective 'black-box' model (Random Forest). These models achieved sensitivity, specificity, and positive predictive value (PPV) exceeding 70%, with an area under the curve (AUC) greater than 80%. The cautious prediction models exhibited enhanced performance while maintaining satisfactory coverage (over 50%). Further, when externally validated on a separate cohort from the same hospital-comprising patients from a subsequent time period-the models showed no pragmatically notable decline in performance. CONCLUSIONS: Our results demonstrate the effectiveness of utilizing PROMs as basis to develop ML models for planning assignments to FT procedures. Notably, the application of controllable AI techniques, particularly those based on XAI and cautious prediction, emerges as a promising approach. These techniques provide reliable and interpretable support, essential for informed decision-making in clinical processes.

Enhancing human-AI collaboration: The case of colonoscopy.

Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.

Assessment of Fast-Track Pathway in Hip and Knee Replacement Surgery by Propensity Score Matching on Patient-Reported Outcomes.

Total hip (THA) and total knee (TKA) arthroplasty procedures have steadily increased over the past few decades, and their use is expected to grow further, mainly due to an increasing number of elderly patients. Cost-containment strategies, supporting a rapid recovery with a positive functional outcomes, high patient satisfaction, and enhanced patient reported outcomes, are needed. A Fast Track surgical procedure (FT) is a coordinated perioperative approach aimed at expediting early mobilization and recovery following surgery and, accordingly, shortening the length of hospital stay (LOS), convalescence and costs. In this view, rapid rehabilitation surgery optimizes traditional rehabilitation methods by integrating evidence-based practices into the procedure. The aim of the present study was to compare the effectiveness of Fast Track versus Care-as-Usual surgical procedures and pathways (including rehabilitation) on a mid-term patient-reported outcome (PROs), the SF12 (with regard both to Physical and Mental Scores), 3 months after hip or knee replacement surgery, with the use of Propensity score-matching (PSM) analysis to address the issue of the comparability of the groups in a non-randomized study. We were interested in the evaluation of the entire pathways, including the postoperative rehabilitation stage, therefore, we only used early home discharge as a surrogate to differentiate between the Fast Track and Care-as-Usual rehabilitation pathways. Our study shows that the entire Fast Track pathway, which includes the post-operative rehabilitation stage, has a significantly positive impact on physical health-related status (SF12 Physical Scores), as perceived by patients 3 months after hip or knee replacement surgery, as opposed to the standardized program, both in terms of the PROs score and the relative improvements observed, as compared with the minimum clinically important difference. This result encourages additional research into the effects of Fast Track rehabilitation on the entire process of care for patients undergoing hip or knee arthroplasty, focusing only on patient-reported outcomes.

Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories.

OBJECTIVES: The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, and there is little attention given to AI integration into laboratory medicine. METHODS: We dedicated a session at the 3rd annual European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) strategic conference in 2022 to the topic of AI in the laboratory of the future. The speakers collaborated on generating a concise summary of the content that is presented in this paper. RESULTS: The five key messages are (1) Laboratory specialists and technicians will continue to improve the analytical portfolio, diagnostic quality and laboratory turnaround times; (2) The modularized nature of laboratory processes is amenable to AI solutions; (3) Laboratory sub-specialization continues and from test selection to interpretation, tasks increase in complexity; (4) Expertise in AI implementation and partnerships with industry will emerge as a professional competency and require novel educational strategies for broad implementation; and (5) regulatory frameworks and guidances have to be adopted to new computational paradigms. CONCLUSIONS: In summary, the speakers opine that the ability to convert the value-proposition of AI in the laboratory will rely heavily on hands-on expertise and well designed quality improvement initiative from within laboratory for improved patient care.

Detecting the Effect Size of Weather Conditions on Patient-Reported Outcome Measures (PROMs).

One of the next frontiers in medical research, particularly in orthopaedic surgery, is personalized treatment outcome prediction. In personalized medicine, treatment choices are adjusted for the patient based on the individual's and their disease's distinct features. A high-value and patient-centered health care system requires evaluating results that integrate the patient's viewpoint. Patient-reported outcome measures (PROMs) are widely used to shed light on patients' perceptions of their health status after an intervention by using validated questionnaires. The aim of this study is to examine whether meteorological or light (night vs. day) conditions affect PROM scores and hence indirectly affect health-related outcomes. We collected scores for PROMs from questionnaires completed by patients (N = 2326) who had undergone hip and knee interventions between June 2017 and May 2020 at the IRCCS Orthopaedic Institute Galeazzi (IOG), Milan, Italy. Nearest neighbour propensity score (PS) matching was applied to ensure the similarity of the groups tested under the different weather-related conditions. The exposure PS was derived through logistic regression. The data were analysed using statistical tests (Student's t-test and Mann-Whitney U test). According to Cohen's effect size, weather conditions may affect the scores for PROMs and, indirectly, health-related outcomes via influencing the relative humidity and weather-related conditions. The findings suggest avoiding PROMs' collection in certain conditions if the odds of outcome-based underperformance are to be minimized. This would ensure a balance between costs for PROMs' collection and data availability.

Application of Machine Learning to Improve Appropriateness of Treatment in an Orthopaedic Setting of Personalized Medicine.

The rise of personalized medicine and its remarkable advancements have revealed new requirements for the availability of appropriate medical decision-making models. Computer science is an area that plays an essential role in the field of personalized medicine, where one of the goals is to provide algorithms and tools to extrapolate knowledge and improve the decision-support process. The minimum clinically important difference (MCID) is the smallest change in PROM scores that patients perceive as meaningful. Treatment that does not achieve the minimum level of improvement is considered inappropriate as well as a potential waste of resources. Using the MCID threshold to identify patients who fail to achieve the minimum change in PROM that results in a meaningful outcome may aid in pre-surgical shared decision-making. The decision tree algorithm is a method for extracting valuable information and providing further meaningful information to the domain expert that supports the decision-making. In the present study, different tools based on machine learning were developed. On the one hand, we compared three XGBoost models to predict the non-achievement of the MCID at six months post-operation in the SF-12 physical score. The prediction score threshold was set to 0.75 to provide three decision-making areas on the basis of the high confidence (HC) intervals; the minority class was re-balanced by weighting the positive class to penalize the loss function (XGBoost cost-sensitive), oversampling the minority class (XGBoost with SMOTE), and re-sampling the negative class (XGBoost with undersampling). On the other hand, we modeled the data through a decision tree (assessment tree), based on different complexity levels, to identify the hidden pattern and to provide a new way to understand possible relationships between the gathered features and the several outcomes. The results showed that all the proposed models were effective as binary classifiers, as they showed moderate predictive performance both regarding the minority or positive class (i.e., our targeted patients, those who will not benefit from surgery) and the negative class. The decision tree visualization can be exploited during the patient assessment status to better understand if those patients will benefit or not from the medical intervention. Both of these tools can come in handy for increasing knowledge about the patient's psychophysical state and for creating an increasingly specialized assessment of the individual patient.

The multicenter European Biological Variation Study (EuBIVAS): a new glance provided by the Principal Component Analysis (PCA), a machine learning unsupervised algorithms, based on the basic metabolic panel linked measurands.

OBJECTIVES: The European Biological Variation Study (EuBIVAS), which includes 91 healthy volunteers from five European countries, estimated high-quality biological variation (BV) data for several measurands. Previous EuBIVAS papers reported no significant differences among laboratories/population; however, they were focused on specific set of measurands, without a comprehensive general look. The aim of this paper is to evaluate the homogeneity of EuBIVAS data considering multivariate information applying the Principal Component Analysis (PCA), a machine learning unsupervised algorithm. METHODS: The EuBIVAS data for 13 basic metabolic panel linked measurands (glucose, albumin, total protein, electrolytes, urea, total bilirubin, creatinine, phosphatase alkaline, aminotransferases), age, sex, menopause, body mass index (BMI), country, alcohol, smoking habits, and physical activity, have been used to generate three databases developed using the traditional univariate and the multivariate Elliptic Envelope approaches to detect outliers, and different missing-value imputations. Two matrix of data for each database, reporting both mean values, and "within-person BV" (CVP) values for any measurand/subject, were analyzed using PCA. RESULTS: A clear clustering between males and females mean values has been identified, where the menopausal females are closer to the males. Data interpretations for the three databases are similar. No significant differences for both mean and CVPs values, for countries, alcohol, smoking habits, BMI and physical activity, have been found. CONCLUSIONS: The absence of meaningful differences among countries confirms the EuBIVAS sample homogeneity and that the obtained data are widely applicable to deliver APS. Our data suggest that the use of PCA and the multivariate approach may be used to detect outliers, although further studies are required.

Applications of deep learning in dentistry.

Over the last few years, translational applications of so-called artificial intelligence in the field of medicine have garnered a significant amount of interest. The present article aims to review existing dental literature that has examined deep learning, a subset of machine learning that has demonstrated the highest performance when applied to image processing and that has been tested as a formidable diagnostic support tool through its automated analysis of radiographic/photographic images. Furthermore, the article will critically evaluate the literature to describe potential methodological weaknesses of the studies and the need for further development. This review includes 28 studies that have described the applications of deep learning in various fields of dentistry. Research into the applications of deep learning in dentistry contains claims of its high accuracy. Nonetheless, many of these studies have substantial limitations and methodological issues (e.g., examiner reliability, the number of images used for training/testing, the methods used for validation) that have significantly limited the external validity of their results. Therefore, future studies that acknowledge the methodological limitations of existing literature will help to establish a better understanding of the usefulness of applying deep learning in dentistry.

Routine blood tests as an active surveillance to monitor COVID-19 prevalence. A retrospective study.

BACKGROUND: In Italy, one of the country most affected by the COVID-19 pandemic, the first autochthonous case appeared in Lombardy on February 20th, 2020. One month later, the number of -COVID-19 patients in Lombardy exceeded 17000 and about 3500 had died. Because of this rapid increase in infected people scientists wonder whether SARS-CoV-2 was already highly circulating in Lombardy before such date. Plasma levels of aspartate aminotransferase (AST) and lactate dehydrogenase (LDH) were shown to be -highly increased in COVID-19 patients. Monitoring their levels in Emergency Room patients during the months preceding February 20th, 2020, might shade light on the prevalence of the disease in the pre-COVID-19 period. METHODS: We retrospectively analyzed the AST and LDH levels from more than 30.000 patients admitted to the San Raffaele Hospital Emergency Room (ER) between September 2019 and May 2020 as well as between September 2018 and May 2019. The number of patients diagnosed with respiratory tract diseases were also analyzed. RESULTS: Data showed that the ER averaged AST and LDH levels are highly sensitive to the presence of COVID-19 patients. During, the months preceding February 20th, 2020, AST and LDH levels, as well as the number of patients diagnosed with respiratory tract diseases were similar to their 2019 counterparts. CONCLUSIONS: No significant evidence showing that a large number of COVID-19 patients were admitted to the San Raffaele Hospital ER before February 20th, 2020, was found. Thus, the virus was likely circulating, within the Hospital catchment area, either in low amounts or through asymptomatic individuals. Because of the high LDH and AST levels' variations induced by COVID-19, routine blood tests might be exploited as a surveillance indicator for a possible second wave.

Assessment and prediction of spine surgery invasiveness with machine learning techniques.

BACKGROUND: The interest in Minimally Invasive Surgery (MIS) techniques has greatly increased in the recent years due to their significant advantages, both in terms of outcome improvement and cost reduction. Also in spine surgery, MIS is now applicable to several conditions and, above all, in low back pain (LBP) treatment. However, reliable and objective measures of invasiveness, necessary to compare different procedures, are still lacking. METHODS: In this article we study the application of Machine Learning (ML) techniques to define an invasiveness score for LBP procedures based on biological markers and inflammatory profiles. In so doing, we can assess the invasiveness of surgical procedures. We also propose a predictive model for treatment planning based on the evaluation of invasiveness of surgical alternatives for specific patients, using their pre-surgery biomarkers. The data used in study was characterized by low sample size and high-dimensionality, thus we adopted a combination of feature selection, careful selection of ML models and conservative model selection choices in order to address these concerns. We also performed an external validation based on a statistically significantly different datasets in order to confirm the relevance of the findings. RESULTS: We report the results of an experimental study on real-world data, for which we obtained promising results for both considered applications: we report an AUC of 0.87 for the task of invasiveness score definition, and an AUC of 0.76 for the invasiveness prediction task. The results obtained on the external validation were in agreement with the obtained results. Further, in both cases the performances were considered as excellent by the involved clinicians and the selected predictive features were biologically relevant and associated with invasiveness and biological impact in the relevant literature. CONCLUSION: Our results show that ML techniques could be effectively employed not only for diagnosis or prognosis, but also for treatment planning, a task of fundamental importance toward personalized and value-based healthcare. These results also show that ML approaches could be effectively used even in scenarios (e.g. pilot studies) where only small samples are available.

PROs in the wild: Assessing the validity of patient reported outcomes in an electronic registry.

BACKGROUND AND OBJECTIVES: Collecting Patient-Reported Outcomes (PROs) is an important way to get first-hand information by patients on the outcome of treatments and surgical procedure they have undergone, and hence about the quality of the care provided. However, the quality of PRO data cannot be given for granted and cannot be traced back to the dimensions of timeliness and completeness only. While the reliability of these data can be guaranteed by adopting standard and validated questionnaires that are used across different health care facilities all over the world, these facilities must take responsibility to assess, monitor and ensure the validity of PROs that are collected from their patients. Validity is affected by biases that are hidden in the data collected. This contribution is then aimed at measuring bias in PRO data, for the impact that these data can have on clinical research and post-marketing surveillance. METHODS: We considered the main biases that can affect PRO validity: Response bias, in terms of Acquiescence bias and Fatigue bias; and Non-Response bias. To assess Acquiescence bias, phone interviews and online surveys were compared, adjusted by age. To assess Fatigue bias, we proposed a specific item about session length and compared PROs scores stratifying according to the responses to this item. We also calculated the intra-patient agreement by conceiving an intra-interview test-retest. To assess Non-Response bias, we considered patients who participated after the saturation of the response-rate curve as proxy of potential non respondents and compared the outcomes in these two strata. All methods encompassed common statistical techniques and are cost-effective at any facility collecting PRO data. RESULTS: Acquiescence bias resulted in significantly different scores between patients reached by either phone or email. In regard to Fatigue bias, stratification by perceived fatigue resulted in contrasting results. A relevant difference was found in intra-patient agreement and an increasing difference in average scores as a function of interview length (or completion time). In regard to Non-Response bias, we found non-significant differences both in scores and variance. CONCLUSIONS: In this paper, we present a set of cost-effective techniques to assess the validity of retrospective PROs data and share some lessons learnt from their application at a large teaching hospital specialized in musculoskeletal disorders that collects PRO data in the follow-up phase of surgery performed therein. The main finding suggests that response bias can affect the PRO validity. Further research on the effectiveness of simple and cost-effective solutions is necessary to mitigate these biases and improve the validity of PRO data.

Spine surgery registries: hope for evidence-based spinal care?

This study aimed to describe the perceptions of decision-makers in major orthopedic centers regarding the value, implementation and use of spine surgery registries. A 33-item survey was sent to CEOs and heads of spine surgery of the International Society of Orthopedic Centers (ISOC). ISOC includes 21 hospitals worldwide with a special focus on high-quality musculoskeletal care. Twelve out of 20 member centers (60%) replied to the survey. Seven have working registries; 5 in Europe and 2 in North America. The estimations for the cost/year were distributed more evenly: $10,000 [2], $20,000 [1], $50,000 [1]. Society cannot afford unnecessary surgery nor renounce to cure patients with effective treatments. Spine surgery registries provide high levels of evidence. The cost of implementing a registry is limited in comparison to RCTs. Spine registries can pragmatically fill our knowledge gap by turning every operated patient into a study participant.

Collecting Patient Reported Outcomes in the Wild: Opportunities and Challenges.

Collecting Patient Reported Outcomes (PROs) is generally seen as an effective way to assess the efficacy and appropriateness of medical interventions, from the patients' perspective. In 2016 the Galeazzi Orthopaedic Institute established a digitized program of PROs collection from spine, hip and knee surgery patients. In this work, we re-port the findings from the data analysis of the responses collected so far about the complementarity of PROs with respect to the data reported by the clinicians, and about the main biases that can undermine their validity and reliability. Although PROs collection is recognized as being far more complex than just asking the patients "how they feel" on a regular basis and it entails costs and devoted electronic platforms, we advocate their further diffusion for the assessment of health technology and clinical procedures.

Minimal Important Difference in Outcome of Disc Degenerative Disease Treatment: The Patients' Perspective.

Evaluation of treatments effectiveness in a context of value-based health care is based on outcomes, and in their assessment. The patient perspective is gaining renovated interest, as demonstrated by the increasing diffusion of Patient Reported Outcome Measure (PROMs) collection initiatives. The concept of Minimal Clinically Important Dif-ference (MID) is generally seen as the basis to estimate the actual effect perceived by the patient after a treatment, like a surgical intervention, but a universally recognized threshold has not yet been established. At the Orthopedic Institute Galeazzi (Milan, Italy) we began a digitized program of PROM collection in spine surgery by means of a digital platform, called Datareg. In this work we aim to investigate MID in the treatment of degenerated disc in terms of patients' perceptions as these are collected through the above electronic registry. We proposed a computation of MID on the basis of two PROM scores, and a critical comparison with a domain expert's proposal.

Morphological and molecular characterization of human hamstrings shows that tendon features are not influenced by donor age.

PURPOSE: Age-related modifications of tendons, such as reduced tenocyte proliferation and modified extracellular matrix (ECM) turnover, have been previously described, but results are often incomplete and discordant. The aim of this study was to investigate, using morphological and molecular methods, the effect of ageing on human tendons and tenocytes, especially focusing on the collagen turnover pathways, in order to understand how the ageing process could influence tendon biology and structure. METHODS: Morphological analysis was performed on fragments from human semitendinosus and gracilis tendons harvested from 10 adult (mean age 41.8 ± 13.3 years) and 6 aged healthy patients (mean age 72.7 ± 7.0 years) by haematoxylin and eosin, Sirius red and Alcian blue staining. The expression of genes and proteins involved in collagen turnover and focal adhesions was assessed by real-time PCR, slot blot and zymography in cultured tenocytes. Cytoskeleton arrangement was studied by immunofluorescence and cell migration by wound healing assay. RESULTS: The structure and composition of ECM in ageing tendons are preserved as well as the expression of genes and proteins involved in collagen turnover pathways. Although morphological analysis revealed that ageing tenocytes tended to an impaired migration potential and that actin filaments are occasionally shorter and randomly distributed, the expression of proteins involved in focal adhesion formation is preserved. CONCLUSION: Results of this study suggest that the structure of ageing tendons is preserved and that ageing tenocytes maintain their ability for ECM remodelling, supporting the hypothesis that ageing tendons maintain their biomechanical properties. The biological reliability of aged tendons has a clinical relevance, supporting the use of tendon autografts also in the elderly patients. Since the common and successful orthopaedic procedure of anterior cruciate ligament reconstruction using either autografts or allografts is becoming more common in older age groups, these findings suggest that the donor age would not significantly influence the clinical outcome.

What Arthroscopic Skills Need to Be Trained Before Continuing Safe Training in the Operating Room?

The purpose of this study was to generate consensus among experienced surgeons on "what skills a resident should possess before continuing safe training in the operating room (OR)." An online survey of 65 questions was developed and distributed to surgeons in the European community. A total of 216 responded. The survey included 15 questions regarding generic and specific skills; 16 on patient and tissue manipulation, 11 on knowledge of pathology and 6 on inspection of e-anatomical structures; 5 methods to prepare residents; and 12 on specific skills exercises. The importance of each question (arthroscopic skill) was evaluated ranging from 1 (not important at all) to 6 (very important). Chi-square test, respondent agreement, and a qualitative ranking method were determined to identify the top ranked skills (p < 0.05). The top four of general skills considered important were "anatomical knowledge," "tissue manipulation," "spatial perception," and "triangulation" (all chi-square test > 134, p < 0.001, all excellent agreement > 0.85, and all "high priority" level). The top ranked 2 specific arthroscopic skills were "portal placement" and "triangulating the tip of the probe with a 30-degree scope" (chi-square test > 176, p < 0.001, excellent agreement, and assigned high priority). The online survey identified consensus on skills that are considered important for a trainee to possess before continuing training in the OR. Compared with the Canadian colleagues, the European arthroscopy community demonstrated similar ranking.

Management of knee injuries: consensus-based indications from a large community of orthopaedic surgeons.

PURPOSE: To describe preferences and to quantify the amount of agreement among orthopaedic surgeons regarding treatment options for four clinical scenarios of knee pathologies. METHODS: A web-based survey was developed to investigate the attitudes of members of an international association of surgeons specialised in sports traumatology and knee surgery European Society of Sports Traumatology, Knee Surgery and Arthroscopy. RESULTS: The response rate was 40 % (412 questionnaires). An inter-rater agreement score (the Normalised Chi-square based Agreement Nomogram, NX2A) was calculated for each question. For scenario 1, 56-year-old male, degenerative medial compartment on both the femoral and tibial side, the surgical approach was preferred to the conservative approach (p < 0.001). Biological procedures were not considered appropriate, and in this respect, the respondents achieved a moderate degree of agreement (NX2A = 0.68). For scenario 2, 35-year-old male, early knee medial arthritis, the surgical treatment was preferred to conservative treatment (p < 0.001). The traditional closed-wedge tibial osteotomy was not regarded as an appropriate treatment with an excellent degree of agreement among surgeons (NX2A = 0.76). For scenario 3, 46-year-old male, ACL lesion, there was an almost disagreement, as respondents showed no preference between a surgical and conservative approach (NX2A = 0.005). Among surgical treatments, an almost perfect agreement with regard to the appropriateness of arthroscopic single-bundle ACL reconstruction with a semitendinosus/gracilis graft was reached by the surgeons (NX2A = 0.8). For scenario 4, 69-year-old male, diffuse knee arthritis (all the compartments), an almost perfect agreement in favour of a total knee prosthesis was obtained for the management of this scenario (NX2A = 0.85). CONCLUSIONS: Web-based survey can help orthopaedic surgeons discuss and propose indications for clinical practice in the management of some of the most common joint diseases. LEVEL OF EVIDENCE: Cross-sectional survey, Level V.

Infectious and thromboembolic complications of arthroscopic shoulder surgery.

HYPOTHESIS: This study investigates the rate of infectious and thromboembolic complications in shoulder arthroscopy and their association with pharmacologic prophylaxis. MATERIALS AND METHODS: On behalf of the Italian Society for Knee Surgery, Arthroscopy, Sport Traumatology, Cartilage and Orthopaedic Technologies (SIGASCOT), we asked the members to complete an on-line Web survey about their experiences and strategies of prophylaxis in shoulder arthroscopy. RESULTS: In the period 2005-2006, 9385 surgeries were performed. We report 15 infections and 6 DVTs. The overall rate of infections was 0.0016 (1.6/1000) and the rate of DVTs was 0.0006 (0.6/1000) CONCLUSION: The association between infection and antibiotic prophylaxis was significant (P=0.01); however, the risk of DVTs was not decreased with heparin prophylaxis. LEVEL OF EVIDENCE: Level 3.