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Abstract Background A large portion of tissues stored worldwide for diagnostic purposes is formalin-fixed and paraffin-embedded (FFPE). These FFPE-archived tissues are an extremely valuable source for retrospective (genetic) studies. These include mutation screening in cancer-critical genes as well as pathogen detection. In this study we evaluated the impact of several widely used DNA extraction methods on the quality of molecular diagnostics on FFPE tissues. Findings We compared 4 DNA extraction methods from 4 identically processed FFPE mammary-, prostate-, colon- and lung tissues with regard to PCR inhibition, real time SNP detection and amplifiable fragment size. The extraction methods, with and without proteinase K pre-treatment, tested were: 1) heat-treatment, 2) QIAamp DNA-blood-mini-kit, 3) EasyMAG NucliSens and 4) Gentra Capture-Column-kit. Amplifiable DNA fragment size was assessed by multiplexed 200-400-600 bp PCR and appeared highly influenced by the extraction method used. Proteinase K pre-treatment was a prerequisite for proper purification of DNA from FFPE. Extractions with QIAamp, EasyMAG and heat-treatment were found suitable for amplification of fragments up to 400 bp from all tissues, 600 bp amplification was marginally successful (best was QIAamp). QIAamp and EasyMAG extracts were found suitable for downstream real time SNP detection. Gentra extraction was unsuitable. Hands-on time was lowest for heat-treatment, followed by EasyMAG. Conclusions We conclude that the extraction method plays an important role with regard to performance in downstream molecular applications.

Background: Tuberculosis (TB) is amongst the leading causes of death from an infectious disease, with an estimated 1.3 million deaths from TB in 2022. Approximately 25% of the global population is estimated to be infected with the TB bacterium, giving rise to 10.6 million episodes of TB disease in 2022. The prevalence of diabetes influences TB incidence and TB mortality. It is associated not only with an increased risk of TB disease but also death during TB treatment, TB relapse after treatment completion and multidrug-resistant TB. Since 2011, the World Health Organization (WHO) has recommended collaborative TB and diabetes activities as outlined in the Collaborative Framework for Care and Control of TB and Diabetes., Objectives: To determine the prognostic value of diabetes mellitus (DM) in the general population of adults, adolescents and children for predicting tuberculosis disease., Search Methods: We searched the literature databases MEDLINE (via PubMed) and WHO Global Index Medicus, and the WHO International Clinical Trials Registry Platform (ICTRP) on 3 May 2023 (date of last search for all databases); we placed no restrictions on the language of publication., Selection Criteria: We included retrospective and prospective cohort studies, irrespective of publication status or language. The target population comprised adults, adolescents and children from diverse settings, encompassing outpatient and inpatient cohorts, with varying comorbidities and risk of exposure to tuberculosis., Data Collection and Analysis: We used standard Cochrane methodology and the Quality In Prognosis Studies (QUIPS) tool. Prognostic factors assessed at enrolment/baseline included diabetes, as defined by the individual studies, encompassing patient-reported status, abstracted from medical records or claims data, or diagnosed by plasma glucose/glycosylated haemoglobin. The primary outcome was the incidence of tuberculosis disease. The secondary outcome was recurrent TB disease. We performed a random-effects meta-analysis for the adjusted hazard ratios, risk ratios, or odds ratios, employing the restricted maximum likelihood estimation. We rated the certainty of the evidence using the GRADE approach., Main Results: We included 48 cohort studies with over 61 million participants from the six WHO regions. However, the representation was variable as eight population-based studies were from South Korea and 19 from China, with overlapping study periods, and only one from the African region (Ethiopia). All studies included adults, and nine studies also included children and adolescents. Most studies diagnosed DM based on clinical records, including fasting blood glucose levels or glucose-lowering treatments. The studies did not distinguish between type 1 and type 2 DM; only one study focused on type 1 DM. Diagnosis and exclusion of TB were performed using culture or molecular WHO-recommended rapid diagnostic tests (mWRD) in only 12 studies, which could have biassed the effect estimate. The median follow-up time was five years (interquartile range 1.5 to 10, range 1 to 16.9), and the studies primarily reported an adjusted hazard ratio from a multivariable Cox-proportional hazard model. Hazard Ratios (HR) The HR estimates represent the highest certainty of the evidence, explored through sensitivity analyses and excluding studies at high risk of bias. We present 95% confidence intervals (CI) and prediction intervals, which show between-study heterogeneity represented in measuring the variability of effect sizes (i.e. the interval within which the effect size of a new study would fall considering the same population of studies included in the meta-analysis). DM may increase the risk of tuberculosis disease (HR 1.90, 95% CI 1.51 to 2.40; prediction interval 0.83 to 4.39; 10 studies; 11,713,023 participants). The certainty of the evidence is low, due to a moderate risk of bias across studies and inconsistency. Considering a risk without diabetes of 129 cases per 100,000 population, this represents 102 more (59 to 153 more) cases per 100,000. When stratified by follow-up time, the results are more consistent across < 10 years follow-up (HR 1.52, 95% CI 1.47 to 1.57; prediction interval 1.45 to 1.59; 7 studies; 10,380,872 participants). This results in a moderate certainty of the evidence due to a moderate risk of bias across studies. However, at 10 or more years of follow-up, the estimates yield a wider CI and a higher HR (HR 2.44, 95% CI 1.22 to 4.88; prediction interval 0.09 to 69.12; 3 studies; 1,332,151 participants). The certainty of the evidence is low due to the moderate risk of bias and inconsistency. Odds Ratio (OR) DM may increase the odds of tuberculosis disease (OR 1.61, 95% CI 1.27 to 2.04; prediction interval 0.96 to 2.70; 4 studies; 167,564 participants). Stratification by follow-up time was not possible as all studies had a follow-up < 10 years. The certainty of the evidence is low due to a moderate risk of bias and inconsistency. Risk Ratio (RR) The RR estimates represent the highest certainty of the evidence, explored through sensitivity analyses and excluding studies at high risk of bias. DM probably increases the risk of tuberculosis disease (RR 1.60, 95% CI 1.42 to 1.80; prediction interval 1.38 to 1.85; 6 studies; 44,058,675 participants). Stratification by follow-up time was not possible as all studies had a follow-up < 10 years. The certainty of the evidence is moderate due to a moderate risk of bias., Authors' Conclusions: Diabetes probably increases the risk of developing TB disease in the short term (< 10 years) and may also increase the risk in the long term (≥ 10 years). As glycaemic control and access to care may be potential effect modifiers of the association between diabetes and the risk of TB disease, the overall estimates should be interpreted with caution when applied locally. Policies targeted at reducing the burden of diabetes are needed to contribute to the aims of ending TB. Large population-based cohorts, including those derived from high-quality national registries of exposures (diabetes) and outcomes (TB disease), are needed to provide estimates with a high certainty of evidence of this risk across different settings and populations, including low- and middle-income countries from different WHO regions. Moreover, studies including children and adolescents and currently recommended methods for diagnosing TB would provide more up-to-date information relevant to practice and policy., Funding: World Health Organization (203256442) REGISTRATION: PROSPERO registration: CRD42023408807., (Copyright © 2024 The Authors. Cochrane Database of Systematic Reviews published by John Wiley & Sons, Ltd. on behalf of The Cochrane Collaboration.)

Background: Tuberculosis (TB) is a leading cause of mortality due to an infectious disease, with an estimated 1.6 million deaths due to TB in 2022. Approximately 25% of the global population has TB infection, giving rise to 10.6 million episodes of TB disease in 2022. Undernutrition is a key risk factor for TB and was linked to an estimated 2.2 million TB episodes in 2022, as outlined in the World Health Organization (WHO) Global Tuberculosis Report., Objectives: To determine the prognostic value of undernutrition in the general population of adults, adolescents, and children for predicting tuberculosis disease over any time period., Search Methods: We searched the literature databases MEDLINE (via PubMed) and WHO Global Index Medicus, as well as the WHO International Clinical Trials Registry Platform (ICTRP) on 3 May 2023 (date of last search for all databases). We placed no restrictions on the language of publication., Selection Criteria: We included retrospective and prospective cohort studies, irrespective of publication status or language. The target population comprised adults, adolescents, and children from diverse settings, encompassing outpatient and inpatient cohorts, with varying comorbidities and risk of exposure to tuberculosis., Data Collection and Analysis: We used standard Cochrane methodology and the Quality In Prognosis Studies (QUIPS) tool to assess the risk of bias of the studies. Prognostic factors included undernutrition, defined as wasting, stunting, and underweight, with specific measures such as body mass index (BMI) less than two standard deviations below the median for children and adolescents and low BMI scores (< 18.5) for adults and adolescents. Prognostication occurred at enrolment/baseline. The primary outcome was the incidence of TB disease. The secondary outcome was recurrent TB disease. We performed a random-effects meta-analysis for the adjusted hazard ratios (HR), risk ratios (RR), or odds ratios (OR), employing the restricted maximum likelihood estimation. We rated the certainty of the evidence using the GRADE approach., Main Results: We included 51 cohort studies with over 27 million participants from the six WHO regions. Sixteen large population-based studies were conducted in China, Singapore, South Korea, and the USA, and 25 studies focused on people living with HIV, which were mainly conducted in the African region. Most studies were in adults, four in children, and three in children and adults. Undernutrition as an exposure was usually defined according to standard criteria; however, the diagnosis of TB did not include a confirmatory culture or molecular diagnosis using a WHO-approved rapid diagnostic test in eight studies. The median follow-up time was 3.5 years, and the studies primarily reported an adjusted hazard ratio from a multivariable Cox-proportional hazard model. Hazard ratios (HR) The HR estimates represent the highest certainty of the evidence, explored through sensitivity analyses and excluding studies at high risk of bias. We present 95% confidence intervals (CI) and prediction intervals, which present between-study heterogeneity represented in a measurement of the variability of effect sizes (i.e. the interval within which the effect size of a new study would fall considering the same population of studies included in the meta-analysis). Undernutrition may increase the risk of TB disease (HR 2.23, 95% CI 1.83 to 2.72; prediction interval 0.98 to 5.05; 23 studies; 2,883,266 participants). The certainty of the evidence is low due to a moderate risk of bias across studies and inconsistency. When stratified by follow-up time, the results are more consistent across < 10 years follow-up (HR 2.02, 95% CI 1.74 to 2.34; prediction interval 1.20 to 3.39; 22 studies; 2,869,077 participants). This results in a moderate certainty of evidence due to a moderate risk of bias across studies. However, at 10 or more years of follow-up, we found only one study with a wider CI and higher HR (HR 12.43, 95% CI 5.74 to 26.91; 14,189 participants). The certainty of the evidence is low due to the moderate risk of bias and indirectness. Odds ratio (OR) Undernutrition may increase the odds of TB disease, but the results are uncertain (OR 1.56, 95% CI 1.13 to 2.17; prediction interval 0.61 to 3.99; 8 studies; 173,497 participants). Stratification by follow-up was not possible as all studies had a follow-up of < 10 years. The certainty of the evidence is very low due to the high risk of bias and inconsistency. Contour-enhanced funnel plots were not reported due to the few studies included. Risk ratio (RR) Undernutrition may increase the risk of TB disease (RR 1.95, 95% CI 1.72 to 2.20; prediction interval 1.49 to 2.55; 4 studies; 1,475,867 participants). Stratification by follow-up was not possible as all studies had a follow-up of < 10 years. The certainty of the evidence is low due to the high risk of bias. Contour-enhanced funnel plots were not reported due to the few studies included., Authors' Conclusions: Undernutrition probably increases the risk of TB two-fold in the short term (< 10 years) and may also increase the risk in the long term (> 10 years). Policies targeted towards the reduction of the burden of undernutrition are not only needed to alleviate human suffering due to undernutrition and its many adverse consequences, but are also an important part of the critical measures for ending the TB epidemic by 2030. Large population-based cohorts, including those derived from high-quality national registries of exposures (undernutrition) and outcomes (TB disease), are needed to provide high-certainty estimates of this risk across different settings and populations, including low and middle-income countries from different WHO regions. Moreover, studies including children and adolescents and state-of-the-art methods for diagnosing TB would provide more up-to-date information relevant to practice and policy., Funding: World Health Organization (203256442)., Registration: PROSPERO registration: CRD42023408807 Protocol: https://doi.org/10.1002/14651858.CD015890., (Copyright © 2024 The Authors. Cochrane Database of Systematic Reviews published by John Wiley & Sons, Ltd. on behalf of The Cochrane Collaboration.)

Objective: Asthma control is generally monitored by assessing symptoms and lung function. However, optimal treatment is also dependent on the type and extent of airway inflammation. Fraction of exhaled Nitric Oxide (FeNO) is a noninvasive biomarker of type 2 airway inflammation, but its effectiveness in guiding asthma treatment remains disputed. We performed a systematic review and meta-analysis to obtain summary estimates of the effectiveness of FeNO-guided asthma treatment., Design: We updated a Cochrane systematic review from 2016. Cochrane Risk of Bias tool was used to assess risk of bias. Inverse-variance random-effects meta-analysis was performed. Certainty of evidence was assessed using GRADE. Subgroup analyses were performed based on asthma severity, asthma control, allergy/atopy, pregnancy and obesity., Data Sources: The Cochrane Airways Group Trials Register was searched on 9 May 2023., Eligibility Criteria: We included randomized controlled trials (RCTs) comparing the effectiveness of a FeNO-guided treatment versus usual (symptom-guided) treatment in adult asthma patients., Results: We included 12 RCTs (2,116 patients), all showing high or unclear risk of bias in at least one domain. Five RCTs reported support from a FeNO manufacturer. FeNO-guided treatment probably reduces the number of patients having ≥1 exacerbation (OR = 0.61; 95%CI 0.44 to 0.83; six RCTs; GRADE moderate certainty) and exacerbation rate (RR = 0.67; 95%CI 0.54 to 0.82; six RCTs; moderate certainty), and may slightly improve Asthma Control Questionnaire score (MD = -0.10; 95%CI -0.18 to -0.02, six RCTs; low certainty), however, this change is unlikely to be clinically important. An effect on severe exacerbations, quality of life, FEV1, treatment dosage and FeNO values could not be demonstrated. There were no indications that effectiveness is different in subgroups of patients, although evidence for subgroup analysis was limited., Conclusions: FeNO-guided asthma treatment probably results in fewer exacerbations but may not have clinically important effects on other asthma outcomes., (© 2023 The Authors. Clinical & Experimental Allergy published by John Wiley & Sons Ltd.)

Objectives: To explore indicators of the following questionable research practices (QRPs) in randomized controlled trials (RCTs): (1) risk of bias in four domains (random sequence generation, allocation concealment, blinding of participants and personnel, and blinding of outcome assessment); (2) modifications in primary outcomes that were registered in trial registration records (proxy for selective reporting bias); (3) ratio of the achieved to planned sample sizes; and (4) statistical discrepancy., Study Design and Setting: Full texts of all human RCTs published in PubMed in 1996-2017 were automatically identified and information was collected automatically. Potential indicators of QRPs included author-specific, publication-specific, and journal-specific characteristics. Beta, logistic, and linear regression models were used to identify associations between these potential indicators and QRPs., Results: We included 163,129 RCT publications. The median probability of bias assessed using Robot Reviewer software ranged between 43% and 63% for the four risk of bias domains. A more recent publication year, trial registration, mentioning of CONsolidated Standards Of Reporting Trials-checklist, and a higher journal impact factor were consistently associated with a lower risk of QRPs., Conclusion: This comprehensive analysis provides an insight into indicators of QRPs. Researchers should be aware that certain characteristics of the author team and publication are associated with a higher risk of QRPs., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Competing Interests: Competing interests: None declared.

Background: The Revised Cardiac Risk Index (RCRI) is a widely acknowledged prognostic model to estimate preoperatively the probability of developing in-hospital major adverse cardiac events (MACE) in patients undergoing noncardiac surgery. However, the RCRI does not always make accurate predictions, so various studies have investigated whether biomarkers added to or compared with the RCRI could improve this., Objectives: Primary: To investigate the added predictive value of biomarkers to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Secondary: To investigate the prognostic value of biomarkers compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Tertiary: To investigate the prognostic value of other prediction models compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery., Search Methods: We searched MEDLINE and Embase from 1 January 1999 (the year that the RCRI was published) until 25 June 2020. We also searched ISI Web of Science and SCOPUS for articles referring to the original RCRI development study in that period., Selection Criteria: We included studies among adults who underwent noncardiac surgery, reporting on (external) validation of the RCRI and: - the addition of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of the RCRI to other models. Besides MACE, all other adverse outcomes were considered for inclusion., Data Collection and Analysis: We developed a data extraction form based on the CHARMS checklist. Independent pairs of authors screened references, extracted data and assessed risk of bias and concerns regarding applicability according to PROBAST. For biomarkers and prediction models that were added or compared to the RCRI in ≥ 3 different articles, we described study characteristics and findings in further detail. We did not apply GRADE as no guidance is available for prognostic model reviews., Main Results: We screened 3960 records and included 107 articles.   Over all objectives we rated risk of bias as high in ≥ 1 domain in 90% of included studies, particularly in the analysis domain. Statistical pooling or meta-analysis of reported results was impossible due to heterogeneity in various aspects: outcomes used, scale by which the biomarker was added/compared to the RCRI, prediction horizons and studied populations.  Added predictive value of biomarkers to the RCRI Fifty-one studies reported on the added value of biomarkers to the RCRI. Sixty-nine different predictors were identified derived from blood (29%), imaging (33%) or other sources (38%). Addition of NT-proBNP, troponin or their combination improved the RCRI for predicting MACE (median delta c-statistics: 0.08, 0.14 and 0.12 for NT-proBNP, troponin and their combination, respectively). The median total net reclassification index (NRI) was 0.16 and 0.74 after addition of troponin and NT-proBNP to the RCRI, respectively. Calibration was not reported. To predict myocardial infarction, the median delta c-statistic when NT-proBNP was added to the RCRI was 0.09, and 0.06 for prediction of all-cause mortality and MACE combined. For BNP and copeptin, data were not sufficient to provide results on their added predictive performance, for any of the outcomes. Comparison of the predictive value of biomarkers to the RCRI  Fifty-one studies assessed the predictive performance of biomarkers alone compared to the RCRI. We identified 60 unique predictors derived from blood (38%), imaging (30%) or other sources, such as the American Society of Anesthesiologists (ASA) classification (32%). Predictions were similar between the ASA classification and the RCRI for all studied outcomes. In studies different from those identified in objective 1, the median delta c-statistic was 0.15 and 0.12 in favour of  BNP and NT-proBNP alone, respectively, when compared to the RCRI, for the prediction of MACE. For C-reactive protein, the predictive performance was similar to the RCRI. For other biomarkers and outcomes, data were insufficient to provide summary results. One study reported on calibration and none on reclassification. Comparison of the predictive value of other prognostic models to the RCRI   Fifty-two articles compared the predictive ability of the RCRI to other prognostic models. Of these, 42% developed a new prediction model, 22% updated the RCRI, or another prediction model, and 37% validated an existing prediction model. None of the other prediction models showed better performance in predicting MACE than the RCRI. To predict myocardial infarction and cardiac arrest, ACS-NSQIP-MICA had a higher median delta c-statistic of 0.11 compared to the RCRI. To predict all-cause mortality, the median delta c-statistic was 0.15 higher in favour of ACS-NSQIP-SRS compared to the RCRI. Predictive performance was not better for CHADS 2 , CHA 2 DS 2 -VASc, R 2 CHADS 2 , Goldman index, Detsky index or VSG-CRI compared to the RCRI for any of the outcomes. Calibration and reclassification were reported in only one and three studies, respectively., Authors' Conclusions: Studies included in this review suggest that the predictive performance of the RCRI in predicting MACE is improved when NT-proBNP, troponin or their combination are added. Other studies indicate that BNP and NT-proBNP, when used in isolation, may even have a higher discriminative performance than the RCRI. There was insufficient evidence of a difference between the predictive accuracy of the RCRI and other prediction models in predicting MACE. However, ACS-NSQIP-MICA and ACS-NSQIP-SRS outperformed the RCRI in predicting myocardial infarction and cardiac arrest combined, and all-cause mortality, respectively. Nevertheless, the results cannot be interpreted as conclusive due to high risks of bias in a majority of papers, and pooling was impossible due to heterogeneity in outcomes, prediction horizons, biomarkers and studied populations. Future research on the added prognostic value of biomarkers to existing prediction models should focus on biomarkers with good predictive accuracy in other settings (e.g. diagnosis of myocardial infarction) and identification of biomarkers from omics data. They should be compared to novel biomarkers with so far insufficient evidence compared to established ones, including NT-proBNP or troponins. Adherence to recent guidance for prediction model studies (e.g. TRIPOD; PROBAST) and use of standardised outcome definitions in primary studies is highly recommended to facilitate systematic review and meta-analyses in the future., (Copyright © 2021 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.)

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., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Many randomized controlled trials (RCTs) are biased and difficult to reproduce due to methodological flaws and poor reporting. There is increasing attention for responsible research practices and implementation of reporting guidelines, but whether these efforts have improved the methodological quality of RCTs (e.g., lower risk of bias) is unknown. We, therefore, mapped risk-of-bias trends over time in RCT publications in relation to journal and author characteristics. Meta-information of 176,620 RCTs published between 1966 and 2018 was extracted. The risk-of-bias probability (random sequence generation, allocation concealment, blinding of patients/personnel, and blinding of outcome assessment) was assessed using a risk-of-bias machine learning tool. This tool was simultaneously validated using 63,327 human risk-of-bias assessments obtained from 17,394 RCTs evaluated in the Cochrane Database of Systematic Reviews (CDSR). Moreover, RCT registration and CONSORT Statement reporting were assessed using automated searches. Publication characteristics included the number of authors, journal impact factor (JIF), and medical discipline. The annual number of published RCTs substantially increased over 4 decades, accompanied by increases in authors (5.2 to 7.8) and institutions (2.9 to 4.8). The risk of bias remained present in most RCTs but decreased over time for allocation concealment (63% to 51%), random sequence generation (57% to 36%), and blinding of outcome assessment (58% to 52%). Trial registration (37% to 47%) and the use of the CONSORT Statement (1% to 20%) also rapidly increased. In journals with a higher impact factor (>10), the risk of bias was consistently lower with higher levels of RCT registration and the use of the CONSORT Statement. Automated risk-of-bias predictions had accuracies above 70% for allocation concealment (70.7%), random sequence generation (72.1%), and blinding of patients/personnel (79.8%), but not for blinding of outcome assessment (62.7%). In conclusion, the likelihood of bias in RCTs has generally decreased over the last decades. This optimistic trend may be driven by increased knowledge augmented by mandatory trial registration and more stringent reporting guidelines and journal requirements. Nevertheless, relatively high probabilities of bias remain, particularly in journals with lower impact factors. This emphasizes that further improvement of RCT registration, conduct, and reporting is still urgently needed., Competing Interests: The authors have declared that no competing interests exist.

Background: Multiple scores have been proposed to guide risk stratification after percutaneous coronary intervention. This study assessed the performance of the PRECISE-DAPT, PARIS and CREDO-Kyoto risk scores to predict post-discharge ischaemic or bleeding events., Methods: A total of 1491 patients treated with latest-generation drug-eluting stent implantation were evaluated. Risk scores for post-discharge ischaemic or bleeding events were calculated and directly compared. Prognostic performance of both risk scores was assessed with calibration, Harrell's c‑statistics net reclassification index and decision curve analyses., Results: Post-discharge ischaemic events occurred in 56 patients (3.8%) and post-discharge bleeding events in 34 patients (2.3%) within the first year after the invasive procedure. C‑statistics for the PARIS ischaemic risk score was marginal (0.59, 95% confidence interval (CI) 0.51-0.68), whereas the CREDO-Kyoto ischaemic risk score was moderate (0.68, 95% CI 0.60-0.75). With regard to post-discharge bleeding events, CREDO-Kyoto displayed moderate discrimination (c-statistic 0.67, 95% CI 0.56-0.77), whereas PRECISE-DAPT (0.59, 95% CI 0.48-0.69) and PARIS (0.55, 95% CI 0.44-0.65) had a marginal discriminative capacity. Net reclassification index and decision curve analysis favoured CREDO-Kyoto-derived bleeding risk assessment., Conclusion: In this contemporary all-comer population, PARIS and PRECISE-DAPT risk scores were not resilient to independent testing for post-discharge bleeding events. CREDO-Kyoto-derived risk stratification was associated with a moderate predictive capability for post-discharge ischaemic or bleeding events. Future studies are warranted to improve risk stratification with more focus on robustness and rigorous testing.

Background: The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Our 2020 edition of this review showed thoracic (chest) imaging to be sensitive and moderately specific in the diagnosis of coronavirus disease 2019 (COVID-19). In this update, we include new relevant studies, and have removed studies with case-control designs, and those not intended to be diagnostic test accuracy studies., Objectives: To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected COVID-19., Search Methods: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 30 September 2020. We did not apply any language restrictions., Selection Criteria: We included studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19 and that reported estimates of test accuracy or provided data from which we could compute estimates., Data Collection and Analysis: The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using the QUADAS-2 domain-list. We presented the results of estimated sensitivity and specificity using paired forest plots, and we summarised pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. We presented the uncertainty of accuracy estimates using 95% confidence intervals (CIs)., Main Results: We included 51 studies with 19,775 participants suspected of having COVID-19, of whom 10,155 (51%) had a final diagnosis of COVID-19. Forty-seven studies evaluated one imaging modality each, and four studies evaluated two imaging modalities each. All studies used RT-PCR as the reference standard for the diagnosis of COVID-19, with 47 studies using only RT-PCR and four studies using a combination of RT-PCR and other criteria (such as clinical signs, imaging tests, positive contacts, and follow-up phone calls) as the reference standard. Studies were conducted in Europe (33), Asia (13), North America (3) and South America (2); including only adults (26), all ages (21), children only (1), adults over 70 years (1), and unclear (2); in inpatients (2), outpatients (32), and setting unclear (17). Risk of bias was high or unclear in thirty-two (63%) studies with respect to participant selection, 40 (78%) studies with respect to reference standard, 30 (59%) studies with respect to index test, and 24 (47%) studies with respect to participant flow. For chest CT (41 studies, 16,133 participants, 8110 (50%) cases), the sensitivity ranged from 56.3% to 100%, and specificity ranged from 25.4% to 97.4%. The pooled sensitivity of chest CT was 87.9% (95% CI 84.6 to 90.6) and the pooled specificity was 80.0% (95% CI 74.9 to 84.3). There was no statistical evidence indicating that reference standard conduct and definition for index test positivity were sources of heterogeneity for CT studies. Nine chest CT studies (2807 participants, 1139 (41%) cases) used the COVID-19 Reporting and Data System (CO-RADS) scoring system, which has five thresholds to define index test positivity. At a CO-RADS threshold of 5 (7 studies), the sensitivity ranged from 41.5% to 77.9% and the pooled sensitivity was 67.0% (95% CI 56.4 to 76.2); the specificity ranged from 83.5% to 96.2%; and the pooled specificity was 91.3% (95% CI 87.6 to 94.0). At a CO-RADS threshold of 4 (7 studies), the sensitivity ranged from 56.3% to 92.9% and the pooled sensitivity was 83.5% (95% CI 74.4 to 89.7); the specificity ranged from 77.2% to 90.4% and the pooled specificity was 83.6% (95% CI 80.5 to 86.4). For chest X-ray (9 studies, 3694 participants, 2111 (57%) cases) the sensitivity ranged from 51.9% to 94.4% and specificity ranged from 40.4% to 88.9%. The pooled sensitivity of chest X-ray was 80.6% (95% CI 69.1 to 88.6) and the pooled specificity was 71.5% (95% CI 59.8 to 80.8). For ultrasound of the lungs (5 studies, 446 participants, 211 (47%) cases) the sensitivity ranged from 68.2% to 96.8% and specificity ranged from 21.3% to 78.9%. The pooled sensitivity of ultrasound was 86.4% (95% CI 72.7 to 93.9) and the pooled specificity was 54.6% (95% CI 35.3 to 72.6). Based on an indirect comparison using all included studies, chest CT had a higher specificity than ultrasound. For indirect comparisons of chest CT and chest X-ray, or chest X-ray and ultrasound, the data did not show differences in specificity or sensitivity., Authors' Conclusions: Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19. Chest X-ray is moderately sensitive and moderately specific for the diagnosis of COVID-19. Ultrasound is sensitive but not specific for the diagnosis of COVID-19. Thus, chest CT and ultrasound may have more utility for excluding COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest in the same participant population, and implement improved reporting practices., (Copyright © 2021 The Authors. Cochrane Database of Systematic Reviews published by John Wiley & Sons, Ltd. on behalf of The Cochrane Collaboration.)

Background: The diagnosis of infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents major challenges. Reverse transcriptase polymerase chain reaction (RT-PCR) testing is used to diagnose a current infection, but its utility as a reference standard is constrained by sampling errors, limited sensitivity (71% to 98%), and dependence on the timing of specimen collection. Chest imaging tests are being used in the diagnosis of COVID-19 disease, or when RT-PCR testing is unavailable., Objectives: To determine the diagnostic accuracy of chest imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected or confirmed COVID-19., Search Methods: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, and The Stephen B. Thacker CDC Library. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 5 May 2020., Selection Criteria: We included studies of all designs that produce estimates of test accuracy or provide data from which estimates can be computed. We included two types of cross-sectional designs: a) where all patients suspected of the target condition enter the study through the same route and b) where it is not clear up front who has and who does not have the target condition, or where the patients with the target condition are recruited in a different way or from a different population from the patients without the target condition. When studies used a variety of reference standards, we included all of them., Data Collection and Analysis: We screened studies and extracted data independently, in duplicate. We also assessed the risk of bias and applicability concerns independently, in duplicate, using the QUADAS-2 checklist and presented the results of estimated sensitivity and specificity, using paired forest plots, and summarised in tables. We used a hierarchical meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs)., Main Results: We included 84 studies, falling into two categories: studies with participants with confirmed diagnoses of COVID-19 at the time of recruitment (71 studies with 6331 participants) and studies with participants suspected of COVID-19 (13 studies with 1948 participants, including three case-control studies with 549 cases and controls). Chest CT was evaluated in 78 studies (8105 participants), chest X-ray in nine studies (682 COVID-19 cases), and chest ultrasound in two studies (32 COVID-19 cases). All evaluations of chest X-ray and ultrasound were conducted in studies with confirmed diagnoses only. Twenty-five per cent (21/84) of all studies were available only as preprints, 15/71 studies in the confirmed cases group and 6/13 of the studies in the suspected group. Among 71 studies that included confirmed cases, 41 studies had included symptomatic cases only, 25 studies had included cases regardless of their symptoms, five studies had included asymptomatic cases only, three of which included a combination of confirmed and suspected cases. Seventy studies were conducted in Asia, 2 in Europe, 2 in North America and one in South America. Fifty-one studies included inpatients while the remaining 24 studies were conducted in mixed or unclear settings. Risk of bias was high in most studies, mainly due to concerns about selection of participants and applicability. Among the 13 studies that included suspected cases, nine studies were conducted in Asia, and one in Europe. Seven studies included inpatients while the remaining three studies were conducted in mixed or unclear settings. In studies that included confirmed cases the pooled sensitivity of chest CT was 93.1% (95%CI: 90.2 - 95.0 (65 studies, 5759 cases); and for X-ray 82.1% (95%CI: 62.5 to 92.7 (9 studies, 682 cases). Heterogeneity judged by visual assessment of the ROC plots was considerable. Two studies evaluated the diagnostic accuracy of point-of-care ultrasound and both reported zero false negatives (with 10 and 22 participants having undergone ultrasound, respectively). These studies only reported True Positive and False Negative data, therefore it was not possible to pool and derive estimates of specificity. In studies that included suspected cases, the pooled sensitivity of CT was 86.2% (95%CI: 71.9 to 93.8 (13 studies, 2346 participants) and specificity was 18.1% (95%CI: 3.71 to 55.8). Heterogeneity judged by visual assessment of the forest plots was high. Chest CT may give approximately the same proportion of positive results for patients with and without a SARS-CoV-2 infection: the chances of getting a positive CT result are 86% (95% CI: 72 to 94) in patient with a SARS-CoV-2 infection and 82% (95% CI: 44 to 96) in patients without., Authors' Conclusions: The uncertainty resulting from the poor study quality and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results. Our findings indicate that chest CT is sensitive but not specific for the diagnosis of COVID-19 in suspected patients, meaning that CT may not be capable of differentiating SARS-CoV-2 infection from other causes of respiratory illness. This low specificity could also be the result of the poor sensitivity of the reference standard (RT-PCR), as CT could potentially be more sensitive than RT-PCR in some cases. Because of limited data, accuracy estimates of chest X-ray and ultrasound of the lungs for the diagnosis of COVID-19 should be carefully interpreted. Future diagnostic accuracy studies should avoid cases-only studies and pre-define positive imaging findings. Planned updates of this review will aim to: increase precision around the accuracy estimates for CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X rays and ultrasound; and continue to search for studies that fulfil secondary objectives to inform the utility of imaging along different diagnostic pathways., (Copyright © 2020 The Authors. Cochrane Database of Systematic Reviews published by John Wiley & Sons, Ltd. on behalf of The Cochrane Collaboration.)

Background: Chronic lymphocytic leukaemia (CLL) is the most common cancer of the lymphatic system in Western countries. Several clinical and biological factors for CLL have been identified. However, it remains unclear which of the available prognostic models combining those factors can be used in clinical practice to predict long-term outcome in people newly-diagnosed with CLL., Objectives: To identify, describe and appraise all prognostic models developed to predict overall survival (OS), progression-free survival (PFS) or treatment-free survival (TFS) in newly-diagnosed (previously untreated) adults with CLL, and meta-analyse their predictive performances., Search Methods: We searched MEDLINE (from January 1950 to June 2019 via Ovid), Embase (from 1974 to June 2019) and registries of ongoing trials (to 5 March 2020) for development and validation studies of prognostic models for untreated adults with CLL. In addition, we screened the reference lists and citation indices of included studies., Selection Criteria: We included all prognostic models developed for CLL which predict OS, PFS, or TFS, provided they combined prognostic factors known before treatment initiation, and any studies that tested the performance of these models in individuals other than the ones included in model development (i.e. 'external model validation studies'). We included studies of adults with confirmed B-cell CLL who had not received treatment prior to the start of the study. We did not restrict the search based on study design., Data Collection and Analysis: We developed a data extraction form to collect information based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Independent pairs of review authors screened references, extracted data and assessed risk of bias according to the Prediction model Risk Of Bias ASsessment Tool (PROBAST). For models that were externally validated at least three times, we aimed to perform a quantitative meta-analysis of their predictive performance, notably their calibration (proportion of people predicted to experience the outcome who do so) and discrimination (ability to differentiate between people with and without the event) using a random-effects model. When a model categorised individuals into risk categories, we pooled outcome frequencies per risk group (low, intermediate, high and very high). We did not apply GRADE as guidance is not yet available for reviews of prognostic models., Main Results: From 52 eligible studies, we identified 12 externally validated models: six were developed for OS, one for PFS and five for TFS. In general, reporting of the studies was poor, especially predictive performance measures for calibration and discrimination; but also basic information, such as eligibility criteria and the recruitment period of participants was often missing. We rated almost all studies at high or unclear risk of bias according to PROBAST. Overall, the applicability of the models and their validation studies was low or unclear; the most common reasons were inappropriate handling of missing data and serious reporting deficiencies concerning eligibility criteria, recruitment period, observation time and prediction performance measures. We report the results for three models predicting OS, which had available data from more than three external validation studies: CLL International Prognostic Index (CLL-IPI) This score includes five prognostic factors: age, clinical stage, IgHV mutational status, B2-microglobulin and TP53 status. Calibration: for the low-, intermediate- and high-risk groups, the pooled five-year survival per risk group from validation studies corresponded to the frequencies observed in the model development study. In the very high-risk group, predicted survival from CLL-IPI was lower than observed from external validation studies. Discrimination: the pooled c-statistic of seven external validation studies (3307 participants, 917 events) was 0.72 (95% confidence interval (CI) 0.67 to 0.77). The 95% prediction interval (PI) of this model for the c-statistic, which describes the expected interval for the model's discriminative ability in a new external validation study, ranged from 0.59 to 0.83. Barcelona-Brno score Aimed at simplifying the CLL-IPI, this score includes three prognostic factors: IgHV mutational status, del(17p) and del(11q). Calibration: for the low- and intermediate-risk group, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of four external validation studies (1755 participants, 416 events) was 0.64 (95% CI 0.60 to 0.67); 95% PI 0.59 to 0.68. MDACC 2007 index score The authors presented two versions of this model including six prognostic factors to predict OS: age, B2-microglobulin, absolute lymphocyte count, gender, clinical stage and number of nodal groups. Only one validation study was available for the more comprehensive version of the model, a formula with a nomogram, while seven studies (5127 participants, 994 events) validated the simplified version of the model, the index score. Calibration: for the low- and intermediate-risk groups, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of the seven external validation studies for the index score was 0.65 (95% CI 0.60 to 0.70); 95% PI 0.51 to 0.77., Authors' Conclusions: Despite the large number of published studies of prognostic models for OS, PFS or TFS for newly-diagnosed, untreated adults with CLL, only a minority of these (N = 12) have been externally validated for their respective primary outcome. Three models have undergone sufficient external validation to enable meta-analysis of the model's ability to predict survival outcomes. Lack of reporting prevented us from summarising calibration as recommended. Of the three models, the CLL-IPI shows the best discrimination, despite overestimation. However, performance of the models may change for individuals with CLL who receive improved treatment options, as the models included in this review were tested mostly on retrospective cohorts receiving a traditional treatment regimen. In conclusion, this review shows a clear need to improve the conducting and reporting of both prognostic model development and external validation studies. For prognostic models to be used as tools in clinical practice, the development of the models (and their subsequent validation studies) should adapt to include the latest therapy options to accurately predict performance. Adaptations should be timely., (Copyright © 2020 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.)

It is widely recommended that any developed-diagnostic or prognostic-prediction model is externally validated in terms of its predictive performance measured by calibration and discrimination. When multiple validations have been performed, a systematic review followed by a formal meta-analysis helps to summarize overall performance across multiple settings, and reveals under which circumstances the model performs suboptimal (alternative poorer) and may need adjustment. We discuss how to undertake meta-analysis of the performance of prediction models with either a binary or a time-to-event outcome. We address how to deal with incomplete availability of study-specific results (performance estimates and their precision), and how to produce summary estimates of the c -statistic, the observed:expected ratio and the calibration slope. Furthermore, we discuss the implementation of frequentist and Bayesian meta-analysis methods, and propose novel empirically-based prior distributions to improve estimation of between-study heterogeneity in small samples. Finally, we illustrate all methods using two examples: meta-analysis of the predictive performance of EuroSCORE II and of the Framingham Risk Score. All examples and meta-analysis models have been implemented in our newly developed R package "metamisc".

Background: The Framingham risk models and pooled cohort equations (PCE) are widely used and advocated in guidelines for predicting 10-year risk of developing coronary heart disease (CHD) and cardiovascular disease (CVD) in the general population. Over the past few decades, these models have been extensively validated within different populations, which provided mounting evidence that local tailoring is often necessary to obtain accurate predictions. The objective is to systematically review and summarize the predictive performance of three widely advocated cardiovascular risk prediction models (Framingham Wilson 1998, Framingham ATP III 2002 and PCE 2013) in men and women separately, to assess the generalizability of performance across different subgroups and geographical regions, and to determine sources of heterogeneity in the findings across studies., Methods: A search was performed in October 2017 to identify studies investigating the predictive performance of the aforementioned models. Studies were included if they externally validated one or more of the original models in the general population for the same outcome as the original model. We assessed risk of bias for each validation and extracted data on population characteristics and model performance. Performance estimates (observed versus expected (OE) ratio and c-statistic) were summarized using a random effects models and sources of heterogeneity were explored with meta-regression., Results: The search identified 1585 studies, of which 38 were included, describing a total of 112 external validations. Results indicate that, on average, all models overestimate the 10-year risk of CHD and CVD (pooled OE ratio ranged from 0.58 (95% CI 0.43-0.73; Wilson men) to 0.79 (95% CI 0.60-0.97; ATP III women)). Overestimation was most pronounced for high-risk individuals and European populations. Further, discriminative performance was better in women for all models. There was considerable heterogeneity in the c-statistic between studies, likely due to differences in population characteristics., Conclusions: The Framingham Wilson, ATP III and PCE discriminate comparably well but all overestimate the risk of developing CVD, especially in higher risk populations. Because the extent of miscalibration substantially varied across settings, we highly recommend that researchers further explore reasons for overprediction and that the models be updated for specific populations.

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., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Objective: Increased recall rates in the Dutch breast cancer screening program call for a new assessment strategy aiming to reduce unnecessary costs and anxiety. Diagnostic work-up (usual care) includes multidisciplinary hospital assessment and is similar for all recalled women, regardless of the radiologist's suspicion of breast cancer. This is similar in many Canadian settings. We developed a novel assessment strategy that offers women with a low suspicion of breast cancer a quick and non-invasive assessment by a screening radiologist (intervention). We compared these two strategies in a cost-effectiveness analysis based on a randomized controlled trial: multicentre randomized controlled trial (MASS trial, Netherlands National Trial Register: NTR1480)., Methods: Participants were enrolled between August 2010 and December 2012 and were randomly assigned to either the intervention or control group (allocation ratio 2:1). Fourteen assessment centres participated in the study. Questionnaires were used to record quality of life (EuroQol-5D), health care use and costs after recall., Results: Our study comprised 366 women, of whom 288 were randomly assigned to the intervention group and 88 to the control group. The mean difference in cost was €153/CAD $226 (95% confidence interval €107-199/CAD $158-294, p&lt;0.001). We found no significant differences in quality of life. The bootstrapped incremental cost-effectiveness ratios in the cost-effectiveness plane showed that the intervention was the efficient, cost-saving modality., Conclusion: Our data show the benefits of tailoring diagnostic assessment to the screening radiologist's suspicion of breast cancer. Scenarios of implementing such a strategy in the Dutch screening or health care system are currently being discussed with various stakeholders.