Impact of fatigue in surgeons on performance and patient outcome: systematic review.

BACKGROUND: While fatigue is an inevitable aspect of performing surgical procedures, lack of consensus remains on its effect on surgical performance. The aim of this systematic review was to assess the effect of non-muscular fatigue on surgical outcome. METHODS: MEDLINE and Embase were searched up to 17 January 2023. Studies on students, learning, duty-hour restrictions, muscle fatigue, non-surgical or subjective outcome, the weekend effect, or time of admission were excluded. Studies were categorized based on real-life or simulated surgery. The Cochrane risk-of-bias tool was used to assess RCTs and the Newcastle-Ottawa scale was used to assess cohort studies. Due to heterogeneity among studies, data pooling was not feasible and study findings were synthesized narratively. RESULTS: From the 7251 studies identified, 134 studies (including 1 684 073 cases) were selected for analysis (110 real-life studies and 24 simulator studies). Of the simulator studies, 46% (11 studies) reported a deterioration in surgical outcome when fatigue was present, using direct measures of fatigue. In contrast, only 35.5% (39 studies) of real-life studies showed a deterioration, observed in only 12.5% of all outcome measures, specifically involving aggregated surgical outcomes. CONCLUSION: Almost half of simulator studies, along with one-third of real-life studies, consistently report negative effects of fatigue, highlighting a significant concern. The discrepancy between simulator/real-life studies may be explained by heightened motivation and effort investment in real-life studies. Currently, published fatigue and outcome measures, especially in real-life studies, are insufficient to fully define the impact of fatigue on surgical outcomes due to the absence of direct fatigue measures and crude, post-hoc outcome measures.

At some point, surgeons become tired, just like anyone else. While in other jobs, people start to perform worse as they get tired, it is not known whether this is also true for surgeons. It is important to know this because patients may be worse off if their surgeon is tired. The aim of this study was to find out if being tired affects how surgeons do their work. Medical databases were searched through for studies on tired surgeons and the impact of fatigue on their work. Some studies looked at tired surgeons during real surgery and other studies looked at tired surgeons during sessions on surgery simulators. More than 7000 studies were examined and 134 of them were selected. They included over 1.6 million surgeries. Among these studies, 110 investigated real surgeries and 24 looked at simulated surgical sessions. Interestingly, almost half of the studies looking at simulated surgeries found that being tired had a negative effect on the simulated surgery. However, in real surgeries, this happened in only one-third of studies. The difference between real surgery and simulator surgery could be because in real surgeries surgeons always try to do their best, even when fatigued, because they are dealing with real patients. Another reason could be that the tools used to check whether surgeons are tired or whether the surgery went well are not very good. To help both surgeons and patients, there is a need to find better ways to determine if surgeons are truly tired and to make sure the tests are better.

Diagnostic yield and safety of navigation bronchoscopy: A systematic review and meta-analysis.

BACKGROUND: Navigation bronchoscopy has seen rapid development in the past decade in terms of new navigation techniques and multi-modality approaches utilizing different techniques and tools. This systematic review analyses the diagnostic yield and safety of navigation bronchoscopy for the diagnosis of peripheral pulmonary nodules suspected of lung cancer. METHODS: An extensive search was performed in Embase, Medline and Cochrane CENTRAL in May 2022. Eligible studies used cone-beam CT-guided navigation (CBCT), electromagnetic navigation (EMN), robotic navigation (RB) or virtual bronchoscopy (VB) as the primary navigation technique. Primary outcomes were diagnostic yield and adverse events. Quality of studies was assessed using QUADAS-2. Random effects meta-analysis was performed, with subgroup analyses for different navigation techniques, newer versus older techniques, nodule size, publication year, and strictness of diagnostic yield definition. Explorative analyses of subgroups reported by studies was performed for nodule size and bronchus sign. RESULTS: A total of 95 studies (n = 10,381 patients; n = 10,682 nodules) were included. The majority (n = 63; 66.3%) had high risk of bias or applicability concerns in at least one QUADAS-2 domain. Summary diagnostic yield was 70.9% (95%-CI 68.4%-73.2%). Overall pneumothorax rate was 2.5%. Newer navigation techniques using advanced imaging and/or robotics(CBCT, RB, tomosynthesis guided EMN; n = 24 studies) had a statistically significant higher diagnostic yield compared to longer established techniques (EMN, VB; n = 82 studies): 77.5% (95%-CI 74.7%-80.1%) vs 68.8% (95%-CI 65.9%-71.6%) (p < 0.001).Explorative subgroup analyses showed that larger nodule size and bronchus sign presence were associated with a statistically significant higher diagnostic yield. Other subgroup analyses showed no significant differences. CONCLUSION: Navigation bronchoscopy is a safe procedure, with the potential for high diagnostic yield, in particular using newer techniques such as RB, CBCT and tomosynthesis-guided EMN. Studies showed a large amount of heterogeneity, making comparisons difficult. Standardized definitions for outcomes with relevant clinical context will improve future comparability.

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review.

OBJECTIVES: In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction. STUDY DESIGN AND SETTING: We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices. RESULTS: We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion. CONCLUSION: The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence.

How to assess applicability and methodological quality of comparative studies of operative interventions in orthopedic trauma surgery.

PURPOSE: It is challenging to generate and subsequently implement high-quality evidence in surgical practice. A first step would be to grade the strengths and weaknesses of surgical evidence and appraise risk of bias and applicability. Here, we described items that are common to different risk-of-bias tools. We explained how these could be used to assess comparative operative intervention studies in orthopedic trauma surgery, and how these relate to applicability of results. METHODS: We extracted information from the Cochrane risk-of-bias-2 (RoB-2) tool, Risk Of Bias In Non-randomised Studies-of Interventions tool (ROBINS-I), and Methodological Index for Non-Randomized Studies (MINORS) criteria and derived a concisely formulated set of items with signaling questions tailored to operative interventions in orthopedic trauma surgery. RESULTS: The established set contained nine items: population, intervention, comparator, outcome, confounding, missing data and selection bias, intervention status, outcome assessment, and pre-specification of analysis. Each item can be assessed using signaling questions and was explained using good practice examples of operative intervention studies in orthopedic trauma surgery. CONCLUSION: The set of items will be useful to form a first judgment on studies, for example when including them in a systematic review. Existing risk of bias tools can be used for further evaluation of methodological quality. Additionally, the proposed set of items and signaling questions might be a helpful starting point for peer reviewers and clinical readers.

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology.

BACKGROUND: Prognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their clinical uptake in the oncology domain. METHODS: We conducted a systematic review and searched MEDLINE and EMBASE databases for oncology-related studies developing a prognostic model using machine learning methods published between 01/01/2019 and 05/09/2019. The primary outcome was risk of bias, judged using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We described risk of bias overall and for each domain, by development and validation analyses separately. RESULTS: We included 62 publications (48 development-only; 14 development with validation). 152 models were developed across all publications and 37 models were validated. 84% (95% CI: 77 to 89) of developed models and 51% (95% CI: 35 to 67) of validated models were at overall high risk of bias. Bias introduced in the analysis was the largest contributor to the overall risk of bias judgement for model development and validation. 123 (81%, 95% CI: 73.8 to 86.4) developed models and 19 (51%, 95% CI: 35.1 to 67.3) validated models were at high risk of bias due to their analysis, mostly due to shortcomings in the analysis including insufficient sample size and split-sample internal validation. CONCLUSIONS: The quality of machine learning based prognostic models in the oncology domain is poor and most models have a high risk of bias, contraindicating their use in clinical practice. Adherence to better standards is urgently needed, with a focus on sample size estimation and analysis methods, to improve the quality of these models.

Response to Systemic Therapies in Ovarian Adult Granulosa Cell Tumors: A Literature Review.

For adult granulosa cell tumors (aGCTs), the preferred treatment modality is surgery. Chemotherapy and anti-hormonal therapy are also frequently used in patients with recurrent aGCT. We aimed to review the existing literature on the response to chemotherapy and anti-hormonal therapy in patients with aGCT. Embase and MEDLINE were searched from inception to November 2021 for eligible studies. Objective response rate (ORR) was calculated as the total number of cases with a complete response (CR) or a partial response (PR). Disease control rate (DCR) was defined as the sum of cases with CR, PR or stable disease (SD). A total of 10 studies were included that reported on chemotherapy and 13 studies were included that reported on anti-hormonal therapy. The response rates of the 56 chemotherapy regimens that could be evaluated resulted in an ORR of 30% and DCR of 58%. For anti-hormonal therapy, the results of 73 regimens led to an ORR of 11% and a DCR of 66%. Evidence on systemic therapy in aGCT only is limited. For both chemotherapy and anti-hormonal therapy, the ORR is limited, but the response is considerably higher when patients achieving SD are included. New approaches are needed to provide more evidence and standardize treatment in aGCT.

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.

BACKGROUND: Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology. METHODS: We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models. RESULTS: Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available. CONCLUSIONS: The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved.

OBJECTIVE: Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology. STUDY DESIGN AND SETTING: We conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD. RESULTS: Sixty-two publications met the inclusion criteria. 48 were development studies and 14 were development with validation studies. 152 models were developed across all publications. Median adherence to TRIPOD reporting items was 41% [range: 10%-67%] and at least 50% adherence was found in 19% (n=12/62) of publications. Adherence was lower in development only studies (median: 38% [range: 10%-67%]); and higher in development with validation studies (median: 49% [range: 33%-59%]). CONCLUSION: Reporting of clinical prediction models using machine learning in oncology is poor and needs urgent improvement, so readers and stakeholders can appraise the study methods, understand study findings, and reduce research waste.

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence.

INTRODUCTION: The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques. METHODS AND ANALYSIS: TRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications. PROSPERO REGISTRATION NUMBER: CRD42019140361 and CRD42019161764.

Routine laboratory testing to determine if a patient has COVID-19.

BACKGROUND: Specific diagnostic tests to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and resulting COVID-19 disease are not always available and take time to obtain results. Routine laboratory markers such as white blood cell count, measures of anticoagulation, C-reactive protein (CRP) and procalcitonin, are used to assess the clinical status of a patient. These laboratory tests may be useful for the triage of people with potential COVID-19 to prioritize them for different levels of treatment, especially in situations where time and resources are limited. OBJECTIVES: To assess the diagnostic accuracy of routine laboratory testing as a triage test to determine if a person has COVID-19. SEARCH METHODS: On 4 May 2020 we undertook electronic searches in the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. SELECTION CRITERIA: We included both case-control designs and consecutive series of patients that assessed the diagnostic accuracy of routine laboratory testing as a triage test to determine if a person has COVID-19. The reference standard could be reverse transcriptase polymerase chain reaction (RT-PCR) alone; RT-PCR plus clinical expertise or and imaging; repeated RT-PCR several days apart or from different samples; WHO and other case definitions; and any other reference standard used by the study authors. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data from each included study. They also assessed the methodological quality of the studies, using QUADAS-2. We used the 'NLMIXED' procedure in SAS 9.4 for the hierarchical summary receiver operating characteristic (HSROC) meta-analyses of tests for which we included four or more studies. To facilitate interpretation of results, for each meta-analysis we estimated summary sensitivity at the points on the SROC curve that corresponded to the median and interquartile range boundaries of specificities in the included studies. MAIN RESULTS: We included 21 studies in this review, including 14,126 COVID-19 patients and 56,585 non-COVID-19 patients in total. Studies evaluated a total of 67 different laboratory tests. Although we were interested in the diagnotic accuracy of routine tests for COVID-19, the included studies used detection of SARS-CoV-2 infection through RT-PCR as reference standard. There was considerable heterogeneity between tests, threshold values and the settings in which they were applied. For some tests a positive result was defined as a decrease compared to normal vaues, for other tests a positive result was defined as an increase, and for some tests both increase and decrease may have indicated test positivity. None of the studies had either low risk of bias on all domains or low concerns for applicability for all domains. Only three of the tests evaluated had a summary sensitivity and specificity over 50%. These were: increase in interleukin-6, increase in C-reactive protein and lymphocyte count decrease. Blood count Eleven studies evaluated a decrease in white blood cell count, with a median specificity of 93% and a summary sensitivity of 25% (95% CI 8.0% to 27%; very low-certainty evidence). The 15 studies that evaluated an increase in white blood cell count had a lower median specificity and a lower corresponding sensitivity. Four studies evaluated a decrease in neutrophil count. Their median specificity was 93%, corresponding to a summary sensitivity of 10% (95% CI 1.0% to 56%; low-certainty evidence). The 11 studies that evaluated an increase in neutrophil count had a lower median specificity and a lower corresponding sensitivity. The summary sensitivity of an increase in neutrophil percentage (4 studies) was 59% (95% CI 1.0% to 100%) at median specificity (38%; very low-certainty evidence). The summary sensitivity of an increase in monocyte count (4 studies) was 13% (95% CI 6.0% to 26%) at median specificity (73%; very low-certainty evidence). The summary sensitivity of a decrease in lymphocyte count (13 studies) was 64% (95% CI 28% to 89%) at median specificity (53%; low-certainty evidence). Four studies that evaluated a decrease in lymphocyte percentage showed a lower median specificity and lower corresponding sensitivity. The summary sensitivity of a decrease in platelets (4 studies) was 19% (95% CI 10% to 32%) at median specificity (88%; low-certainty evidence). Liver function tests The summary sensitivity of an increase in alanine aminotransferase (9 studies) was 12% (95% CI 3% to 34%) at median specificity (92%; low-certainty evidence). The summary sensitivity of an increase in aspartate aminotransferase (7 studies) was 29% (95% CI 17% to 45%) at median specificity (81%) (low-certainty evidence). The summary sensitivity of a decrease in albumin (4 studies) was 21% (95% CI 3% to 67%) at median specificity (66%; low-certainty evidence). The summary sensitivity of an increase in total bilirubin (4 studies) was 12% (95% CI 3.0% to 34%) at median specificity (92%; very low-certainty evidence). Markers of inflammation The summary sensitivity of an increase in CRP (14 studies) was 66% (95% CI 55% to 75%) at median specificity (44%; very low-certainty evidence). The summary sensitivity of an increase in procalcitonin (6 studies) was 3% (95% CI 1% to 19%) at median specificity (86%; very low-certainty evidence). The summary sensitivity of an increase in IL-6 (four studies) was 73% (95% CI 36% to 93%) at median specificity (58%) (very low-certainty evidence). Other biomarkers The summary sensitivity of an increase in creatine kinase (5 studies) was 11% (95% CI 6% to 19%) at median specificity (94%) (low-certainty evidence). The summary sensitivity of an increase in serum creatinine (four studies) was 7% (95% CI 1% to 37%) at median specificity (91%; low-certainty evidence). The summary sensitivity of an increase in lactate dehydrogenase (4 studies) was 25% (95% CI 15% to 38%) at median specificity (72%; very low-certainty evidence). AUTHORS' CONCLUSIONS: Although these tests give an indication about the general health status of patients and some tests may be specific indicators for inflammatory processes, none of the tests we investigated are useful for accurately ruling in or ruling out COVID-19 on their own. Studies were done in specific hospitalized populations, and future studies should consider non-hospital settings to evaluate how these tests would perform in people with milder symptoms.

GRADE guidelines: 22. The GRADE approach for tests and strategies-from test accuracy to patient-important outcomes and recommendations.

OBJECTIVES: This article describes the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group's framework of moving from test accuracy to patient or population-important outcomes. We focus on the common scenario when studies directly evaluating the effect of diagnostic and other tests or strategies on health outcomes are not available or are not providing the best available evidence. STUDY DESIGN AND SETTING: Using practical examples, we explored how guideline developers and other decision makers can use information from test accuracy to develop a recommendation by linking evidence that addresses downstream consequences. Guideline panels should develop an analytic framework that summarizes the actions that follow from applying a test and the consequences. RESULTS: We describe GRADE's current thinking about the overall certainty of the evidence (also known as quality of the evidence or confidence in the estimates) arising from consideration of the often complex pathways that involve multiple tests and management options. Each link in the evidence can-and often does-lower the overall certainty of the evidence required to formulate recommendations and make decisions about tests. The frequency with which an outcome occurs and its importance will influence whether or not a particular step in the linked evidence is critical to decision-making. CONCLUSIONS: Overall certainty may be expressed by the weakest critical step in the linked evidence. The linked approach to addressing optimal testing will often require the use of decision analytic approaches. We present an example that involves decision modeling in a GRADE Evidence to Decision framework for cervical cancer screening. However, because resources and time of guideline developers may be limited, we describe alternative, pragmatic strategies for developing recommendations addressing test use.

Introducing PALETTE: an iterative method for conducting a literature search for a review in palliative care.

BACKGROUND: In the rapidly developing specialty of palliative care, literature reviews have become increasingly important to inform and improve the field. When applying widely used methods for literature reviews developed for intervention studies onto palliative care, challenges are encountered such as the heterogeneity of palliative care in practice (wide range of domains in patient characteristics, stages of illness and stakeholders), the explorative character of review questions, and the poorly defined keywords and concepts. To overcome the challenges and to provide guidance for researchers to conduct a literature search for a review in palliative care, Palliative cAre Literature rEview iTeraTive mEthod (PALLETE), a pragmatic framework, was developed. We assessed PALETTE with a detailed description. METHODS: PALETTE consists of four phases; developing the review question, building the search strategy, validating the search strategy and performing the search. The framework incorporates different information retrieval techniques: contacting experts, pearl growing, citation tracking and Boolean searching in a transparent way to maximize the retrieval of literature relevant to the topic of interest. The different components and techniques are repeated until no new articles are qualified for inclusion. The phases within PALETTE are interconnected by a recurrent process of validation on 'golden bullets' (articles that undoubtedly should be part of the review), citation tracking and concept terminology reflecting the review question. To give insight in the value of PALETTE, we compared PALETTE with the recommended search method for reviews of intervention studies. RESULTS: By using PALETTE on two palliative care literature reviews, we were able to improve our review questions and search strategies. Moreover, in comparison with the recommended search for intervention reviews, the number of articles needed to be screened was decreased whereas more relevant articles were retrieved. Overall, PALETTE helped us in gaining a thorough understanding of the topic of interest and made us confident that the included studies comprehensively represented the topic. CONCLUSIONS: PALETTE is a coherent and transparent pragmatic framework to overcome the challenges of performing a literature review in palliative care. The method enables researchers to improve question development and to maximise both sensitivity and precision in their search process.

Editor's Choice - Spinal Cord Ischaemia in Endovascular Thoracic and Thoraco-abdominal Aortic Repair: Review of Preventive Strategies.

INTRODUCTION: The incidence of spinal cord ischaemia (SCI) and subsequent paraplegia after thoracic endovascular aneurysm repair (TEVAR) and thoraco-abdominal endovascular aneurysm repair is estimated to be between 2.5% and 8%. The aim of this review is to provide an overview of SCI preventive strategies in TEVAR and thoraco-abdominal repair and recommend an optimal strategy. METHODS: Medline, Embase, and the Cochrane Library were searched for studies on TEVAR, thoraco-abdominal endovascular repair, and the use of SCI preventive measures. The review was reported according to the PRISMA statement. RESULTS: The final analysis included 43 studies (7168 patients). All studies are cohort studies (non-comparative cohorts n = 37, comparative cohorts n = 6) and largely performed retrospectively (n = 27). The included studies had an average MINORS score of 9 (range 6-13) for non-comparative studies and 15.5 (range 12-18) for comparative studies. Transient SCI occurred in 5.7% (450/7,168, 95% CI 4.5-6.9%), permanent SCI in 2.2% (232/7,168, 95% CI 1.6-2.8%). There was a trend towards increased SCI incidence for more "high risk" cohorts. Avoidance of hypotension resulted in a slightly lower permanent SCI rate 1.8% (102/4216, 95% CI 1.2-2.3%) than the overall cohort. A very low SCI estimate (transient and permanent) was found in the subgroup of studies (2 studies, n = 248) using (mild) peri-operative hypothermia (transient SCI 0.8%, permanent SCI 0.4%). In the subgroup using temporary permissive endoleak, there was a transient SCI estimate (15.4%), with a permanent SCI estimate of 4.8%. The remaining preventive measures did not significantly impact transient or permanent SCI estimates. CONCLUSION: Low overall transient and permanent SCI rates are achieved during endovascular thoracic and thoraco-abdominal aortic repair. Based on the presented data, the use of selective spinal fluid drainage in high risk patients seems justified. Peri-operative hypotension should be avoided and treated where possible. The use of mild hypothermia is promising in small cohorts, but requires further evaluation. Further high quality data are essential to establish a definitive preventive strategy.

Facilitating healthcare decisions by assessing the certainty in the evidence from preclinical animal studies.

Laboratory animal studies are used in a wide range of human health related research areas, such as basic biomedical research, drug research, experimental surgery and environmental health. The results of these studies can be used to inform decisions regarding clinical research in humans, for example the decision to proceed to clinical trials. If the research question relates to potential harms with no expectation of benefit (e.g., toxicology), studies in experimental animals may provide the only relevant or controlled data and directly inform clinical management decisions. Systematic reviews and meta-analyses are important tools to provide robust and informative evidence summaries of these animal studies. Rating how certain we are about the evidence could provide important information about the translational probability of findings in experimental animal studies to clinical practice and probably improve it. Evidence summaries and certainty in the evidence ratings could also be used (1) to support selection of interventions with best therapeutic potential to be tested in clinical trials, (2) to justify a regulatory decision limiting human exposure (to drug or toxin), or to (3) support decisions on the utility of further animal experiments. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach is the most widely used framework to rate the certainty in the evidence and strength of health care recommendations. Here we present how the GRADE approach could be used to rate the certainty in the evidence of preclinical animal studies in the context of therapeutic interventions. We also discuss the methodological challenges that we identified, and for which further work is needed. Examples are defining the importance of consistency within and across animal species and using GRADE's indirectness domain as a tool to predict translation from animal models to humans.

Overdiagnosis across medical disciplines: a scoping review.

OBJECTIVE: To provide insight into how and in what clinical fields overdiagnosis is studied and give directions for further applied and methodological research. DESIGN: Scoping review. DATA SOURCES: Medline up to August 2017. STUDY SELECTION: All English studies on humans, in which overdiagnosis was discussed as a dominant theme. DATA EXTRACTION: Studies were assessed on clinical field, study aim (ie, methodological or non-methodological), article type (eg, primary study, review), the type and role of diagnostic test(s) studied and the context in which these studies discussed overdiagnosis. RESULTS: From 4896 studies, 1851 were included for analysis. Half of all studies on overdiagnosis were performed in the field of oncology (50%). Other prevalent clinical fields included mental disorders, infectious diseases and cardiovascular diseases accounting for 9%, 8% and 6% of studies, respectively. Overdiagnosis was addressed from a methodological perspective in 20% of studies. Primary studies were the most common article type (58%). The type of diagnostic tests most commonly studied were imaging tests (32%), although these were predominantly seen in oncology and cardiovascular disease (84%). Diagnostic tests were studied in a screening setting in 43% of all studies, but as high as 75% of all oncological studies. The context in which studies addressed overdiagnosis related most frequently to its estimation, accounting for 53%. Methodology on overdiagnosis estimation and definition provided a source for extensive discussion. Other contexts of discussion included definition of disease, overdiagnosis communication, trends in increasing disease prevalence, drivers and consequences of overdiagnosis, incidental findings and genomics. CONCLUSIONS: Overdiagnosis is discussed across virtually all clinical fields and in different contexts. The variability in characteristics between studies and lack of consensus on overdiagnosis definition indicate the need for a uniform typology to improve coherence and comparability of studies on overdiagnosis.

Comparing Three Different Techniques for Magnetic Resonance Imaging-targeted Prostate Biopsies: A Systematic Review of In-bore versus Magnetic Resonance Imaging-transrectal Ultrasound fusion versus Cognitive Registration. Is There a Preferred Technique?

CONTEXT: The introduction of magnetic resonance imaging-guided biopsies (MRI-GB) has changed the paradigm concerning prostate biopsies. Three techniques of MRI-GB are available: (1) in-bore MRI target biopsy (MRI-TB), (2) MRI-transrectal ultrasound fusion (FUS-TB), and (3) cognitive registration (COG-TB). OBJECTIVE: To evaluate whether MRI-GB has increased detection rates of (clinically significant) prostate cancer (PCa) compared with transrectal ultrasound-guided biopsy (TRUS-GB) in patients at risk for PCa, and which technique of MRI-GB has the highest detection rate of (clinically significant) PCa. EVIDENCE ACQUISITION: We performed a literature search in PubMed, Embase, and CENTRAL databases. Studies were evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 checklist and START recommendations. The initial search identified 2562 studies and 43 were included in the meta-analysis. EVIDENCE SYNTHESIS: Among the included studies 11 used MRI-TB, 17 used FUS-TB, 11 used COG-TB, and four used a combination of techniques. In 34 studies concurrent TRUS-GB was performed. There was no significant difference between MRI-GB (all techniques combined) and TRUS-GB for overall PCa detection (relative risk [RR] 0.97 [0.90-1.07]). MRI-GB had higher detection rates of clinically significant PCa (csPCa) compared with TRUS-GB (RR 1.16 [1.02-1.32]), and a lower yield of insignificant PCa (RR 0.47 [0.35-0.63]). There was a significant advantage (p = 0.02) of MRI-TB compared with COG-TB for overall PCa detection. For overall PCa detection there was no significant advantage of MRI-TB compared with FUS-TB (p=0.13), and neither for FUS-TB compared with COG-TB (p=0.11). For csPCa detection there was no significant advantage of any one technique of MRI-GB. The impact of lesion characteristics such as size and localisation could not be assessed. CONCLUSIONS: MRI-GB had similar overall PCa detection rates compared with TRUS-GB, increased rates of csPCa, and decreased rates of insignificant PCa. MRI-TB has a superior overall PCa detection compared with COG-TB. FUS-TB and MRI-TB appear to have similar detection rates. Head-to-head comparisons of MRI-GB techniques are limited and are needed to confirm our findings. PATIENT SUMMARY: Our review shows that magnetic resonance imaging-guided biopsy detects more clinically significant prostate cancer (PCa) and less insignificant PCa compared with systematic biopsy in men at risk for PCa.

Reported estimates of diagnostic accuracy in ophthalmology conference abstracts were not associated with full-text publication.

OBJECTIVE: To assess whether conference abstracts that report higher estimates of diagnostic accuracy are more likely to reach full-text publication in a peer-reviewed journal. STUDY DESIGN AND SETTING: We identified abstracts describing diagnostic accuracy studies, presented between 2007 and 2010 at the Association for Research in Vision and Ophthalmology (ARVO) Annual Meeting. We extracted reported estimates of sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and diagnostic odds ratio (DOR). Between May and July 2015, we searched MEDLINE and EMBASE to identify corresponding full-text publications; if needed, we contacted abstract authors. Cox regression was performed to estimate associations with full-text publication, where sensitivity, specificity, and AUC were logit transformed, and DOR was log transformed. RESULTS: A full-text publication was found for 226/399 (57%) included abstracts. There was no association between reported estimates of sensitivity and full-text publication (hazard ratio [HR] 1.09 [95% confidence interval {CI} 0.98, 1.22]). The same applied to specificity (HR 1.00 [95% CI 0.88, 1.14]), AUC (HR 0.91 [95% CI 0.75, 1.09]), and DOR (HR 1.01 [95% CI 0.94, 1.09]). CONCLUSION: Almost half of the ARVO conference abstracts describing diagnostic accuracy studies did not reach full-text publication. Studies in abstracts that mentioned higher accuracy estimates were not more likely to be reported in a full-text publication.

Prediction models for cardiovascular disease risk in the general population: systematic review.

OBJECTIVE:  To provide an overview of prediction models for risk of cardiovascular disease (CVD) in the general population. DESIGN:  Systematic review. DATA SOURCES:  Medline and Embase until June 2013. ELIGIBILITY CRITERIA FOR STUDY SELECTION:  Studies describing the development or external validation of a multivariable model for predicting CVD risk in the general population. RESULTS:  9965 references were screened, of which 212 articles were included in the review, describing the development of 363 prediction models and 473 external validations. Most models were developed in Europe (n=167, 46%), predicted risk of fatal or non-fatal coronary heart disease (n=118, 33%) over a 10 year period (n=209, 58%). The most common predictors were smoking (n=325, 90%) and age (n=321, 88%), and most models were sex specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models, and important clinical and methodological information were often missing. The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was heterogeneous and measures such as discrimination and calibration were reported for only 65% and 58% of the external validations, respectively. CONCLUSIONS:  There is an excess of models predicting incident CVD in the general population. The usefulness of most of the models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, in this era of large datasets, future research should focus on externally validating and comparing head-to-head promising CVD risk models that already exist, on tailoring or even combining these models to local settings, and investigating whether these models can be extended by addition of new predictors.