Myopia progression in children during home confinement in the COVID-19 pandemic: A systematic review and meta-analysis

Purpose Myopia is a growing pandemic, especially in children, who risk low vision later in life. Home confinement during the COVID-19 pandemic may have increased myopia progression through increased screentime, decreased time outdoors and increased near work activities. The aim of this study is to compare progression of myopia in children during home confinement period in the COVID-19 pandemic with pre-COVID-19 progression. Methods On January 2023 PubMed, EMBASE and Cochrane were searched for relevant studies. Studies meeting the following criteria were eligible for inclusion: children (under 18 years), home confinement due to COVID-19, spherical equivalent refractive (SER) and axial length (AL) measurements and a follow-up period to measure progression. Quality appraisal was performed by two reviewers independently using the Joanna Briggs Institute tool for cohort studies. Outcomes for myopia were assessed through meta-analysis, analyzing SER (random effects) and AL (fixed effects). Results Hundred and two articles were identified in the search, of which five studies were included in the analysis. Risk of bias is moderate with a few critical flaws in the studies. Myopia progressed more rapidly during the COVID-19 pandemic compared to the pre-COVID-19 period, both in terms of SER (-0.83D [95 %CI, −1.22, −0.43] and AL (0.36 mm [95 %CI, 0.13, 0.39]). Conclusion Progression of myopia during the COVID-19 pandemic accelerated more rapidly compared to the pre-COVID-19 period. Impact of home confinement on myopia may be considered when future lockdown measures are being contemplated. (AU)

Myopia progression in children during home confinement in the COVID-19 pandemic: A systematic review and meta-analysis.

PURPOSE: Myopia is a growing pandemic, especially in children, who risk low vision later in life. Home confinement during the COVID-19 pandemic may have increased myopia progression through increased screentime, decreased time outdoors and increased near work activities. The aim of this study is to compare progression of myopia in children during home confinement period in the COVID-19 pandemic with pre-COVID-19 progression. METHODS: On January 2023 PubMed, EMBASE and Cochrane were searched for relevant studies. Studies meeting the following criteria were eligible for inclusion: children (under 18 years), home confinement due to COVID-19, spherical equivalent refractive (SER) and axial length (AL) measurements and a follow-up period to measure progression. Quality appraisal was performed by two reviewers independently using the Joanna Briggs Institute tool for cohort studies. Outcomes for myopia were assessed through meta-analysis, analyzing SER (random effects) and AL (fixed effects). RESULTS: Hundred and two articles were identified in the search, of which five studies were included in the analysis. Risk of bias is moderate with a few critical flaws in the studies. Myopia progressed more rapidly during the COVID-19 pandemic compared to the pre-COVID-19 period, both in terms of SER (-0.83D [95 %CI, -1.22, -0.43] and AL (0.36 mm [95 %CI, 0.13, 0.39]). CONCLUSION: Progression of myopia during the COVID-19 pandemic accelerated more rapidly compared to the pre-COVID-19 period. Impact of home confinement on myopia may be considered when future lockdown measures are being contemplated.

Predicting sepsis-related mortality and ICU admissions from telephone triage information of patients presenting to out-of-hours GP cooperatives with acute infections: A cohort study of linked routine care databases.

BACKGROUND: General practitioners (GPs) often assess patients with acute infections. It is challenging for GPs to recognize patients needing immediate hospital referral for sepsis while avoiding unnecessary referrals. This study aimed to predict adverse sepsis-related outcomes from telephone triage information of patients presenting to out-of-hours GP cooperatives. METHODS: A retrospective cohort study using linked routine care databases from out-of-hours GP cooperatives, general practices, hospitals and mortality registration. We included adult patients with complaints possibly related to an acute infection, who were assessed (clinic consultation or home visit) by a GP from a GP cooperative between 2017-2019. We used telephone triage information to derive a risk prediction model for sepsis-related adverse outcome (infection-related ICU admission within seven days or infection-related death within 30 days) using logistic regression, random forest, and neural network machine learning techniques. Data from 2017 and 2018 were used for derivation and from 2019 for validation. RESULTS: We included 155,486 patients (median age of 51 years; 59% females) in the analyses. The strongest predictors for sepsis-related adverse outcome were age, type of contact (home visit or clinic consultation), patients considered ABCD unstable during triage, and the entry complaints"general malaise", "shortness of breath" and "fever". The multivariable logistic regression model resulted in a C-statistic of 0.89 (95% CI 0.88-0.90) with good calibration. Machine learning models performed similarly to the logistic regression model. A "sepsis alert" based on a predicted probability >1% resulted in a sensitivity of 82% and a positive predictive value of 4.5%. However, most events occurred in patients receiving home visits, and model performance was substantially worse in this subgroup (C-statistic 0.70). CONCLUSION: Several patient characteristics identified during telephone triage of patients presenting to out-of-hours GP cooperatives were associated with sepsis-related adverse outcomes. Still, on a patient level, predictions were not sufficiently accurate for clinical purposes.

Speech correcting surgery after primary palatoplasty: a systematic literature review and meta-analysis.

OBJECTIVES: In cleft palate patients, the soft palate is commonly closed using straight-line palatoplasty, Z-palatoplasty, or palatoplasty with buccal flaps. Currently, it is unknown which surgical technique is superior regarding speech outcomes. The aim of this review is to study the incidence of speech correcting surgery (SCS) per soft palatoplasty technique and to identify variables which are associated with this outcome. MATERIALS AND METHODS: A systematic literature search was carried out according to the PRISMA guidelines. Inclusion and exclusion criteria were applied to focus on the incidence of SCS after soft palatoplasty. Additional variables like surgical modification, cleft morphology, syndrome, age at palatoplasty, fistula and assessment of velopharyngeal function were reported. A modified New-Ottawa Scale (NOS) was used for quality appraisal. Pooled estimates from the meta-analysis were calculated using a random-effects model. RESULTS: One thousand twenty-nine studies were found of which 54 were included in the analysis. The pooled estimate proportion of SCS after straight-line palatoplasty was 19% (95% CI 15-24), after Z-palatoplasty 6% (95% CI 4-9), and after palatoplasty with buccal flaps 7% (95% CI 4-11). CONCLUSIONS: A lower SCS rate was found in patients receiving Z-palatoplasty when compared to straight-line palatoplasty. We propose a minimum set of outcome parameters which ideally should be included in future studies regarding speech outcomes after cleft palate repair. CLINICAL RELEVANCE: Current literature reports highly heterogenous data regarding cleft palate repair. Our recommended set of parameters may address this inconsistency and could make intercenter comparison possible and of better quality.

Added Diagnostic Value of Biomarkers in Patients with Suspected Sepsis: A Prospective Cohort Study in Out-Of-Hours Primary Care.

BACKGROUND: Point-of-care testing (POCT) has shown promising results in the primary care setting to improve antibiotic therapy in respiratory tract infections and it might also aid general practitioners (GPs) to decide if patients should be referred to a hospital in cases of suspected sepsis. We aimed to assess whether biomarkers with possible POCT use can improve the recognition of sepsis in adults in the primary care setting. METHODS: We prospectively included adult patients with suspected severe infections during out-of-hours home visits. Relevant clinical signs and symptoms were recorded, as well as the biomarkers C-reactive protein, lactate, procalcitonin, high-sensitive troponin I, N-terminal pro b-type natriuretic peptide, creatinine, urea, and pancreatic stone protein. We used a POCT device for lactate only, and the remaining biomarkers were measured in a laboratory from stored blood samples. The primary outcome was sepsis within 72 h of inclusion. The potential of biomarkers to either rule in or rule out sepsis was tested for individual biomarkers combined with a model consisting of signs and symptoms. Net reclassification indices were also calculated. RESULTS: In total, 336 patients, with a median age of 80 years, were included. One hundred forty-one patients (42%) were diagnosed with sepsis. The C statistic for the model with clinical symptoms and signs was 0.84 (95% CI 0.79-0.88). Both lactate and procalcitonin increased the C statistic to 0.85, but none of the biomarkers significantly changed the net reclassification index. CONCLUSIONS: We do not advocate the routine use of POCT in general practice for any of the tested biomarkers of suspected sepsis.

Thoracic imaging tests for the diagnosis of COVID-19.

BACKGROUND: Our March 2021 edition of this review showed thoracic imaging computed tomography (CT) to be sensitive and moderately specific in diagnosing COVID-19 pneumonia. This new edition is an update of the review. OBJECTIVES: Our objectives were to evaluate the diagnostic accuracy of thoracic imaging in people with suspected COVID-19; assess the rate of positive imaging in people who had an initial reverse transcriptase polymerase chain reaction (RT-PCR) negative result and a positive RT-PCR result on follow-up; and evaluate the accuracy of thoracic imaging for screening COVID-19 in asymptomatic individuals. The secondary objective was to assess threshold effects of index test positivity on accuracy. 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 17 February 2021. We did not apply any language restrictions. SELECTION CRITERIA: We included diagnostic accuracy studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19. Studies had to assess chest CT, chest X-ray, or ultrasound of the lungs for the diagnosis of COVID-19, use a reference standard that included RT-PCR, and report estimates of test accuracy or provide data from which we could compute estimates. We excluded studies that used imaging as part of the reference standard and studies that excluded participants with normal index test results. DATA COLLECTION AND ANALYSIS: The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using QUADAS-2. We presented sensitivity and specificity per study on paired forest plots, and summarized pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. MAIN RESULTS: We included 98 studies in this review. Of these, 94 were included for evaluating the diagnostic accuracy of thoracic imaging in the evaluation of people with suspected COVID-19. Eight studies were included for assessing the rate of positive imaging in individuals with initial RT-PCR negative results and positive RT-PCR results on follow-up, and 10 studies were included for evaluating the accuracy of thoracic imaging for imagining asymptomatic individuals. For all 98 included studies, risk of bias was high or unclear in 52 (53%) studies with respect to participant selection, in 64 (65%) studies with respect to reference standard, in 46 (47%) studies with respect to index test, and in 48 (49%) studies with respect to flow and timing. Concerns about the applicability of the evidence to: participants were high or unclear in eight (8%) studies; index test were high or unclear in seven (7%) studies; and reference standard were high or unclear in seven (7%) studies. Imaging in people with suspected COVID-19 We included 94 studies. Eighty-seven studies evaluated one imaging modality, and seven studies evaluated two imaging modalities. All studies used RT-PCR alone or in combination with other criteria (for example, clinical signs and symptoms, positive contacts) as the reference standard for the diagnosis of COVID-19. For chest CT (69 studies, 28285 participants, 14,342 (51%) cases), sensitivities ranged from 45% to 100%, and specificities from 10% to 99%. The pooled sensitivity of chest CT was 86.9% (95% confidence interval (CI) 83.6 to 89.6), and pooled specificity was 78.3% (95% CI 73.7 to 82.3). Definition for index test positivity was a source of heterogeneity for sensitivity, but not specificity. Reference standard was not a source of heterogeneity. For chest X-ray (17 studies, 8529 participants, 5303 (62%) cases), the sensitivity ranged from 44% to 94% and specificity from 24 to 93%. The pooled sensitivity of chest X-ray was 73.1% (95% CI 64. to -80.5), and pooled specificity was 73.3% (95% CI 61.9 to 82.2). Definition for index test positivity was not found to be a source of heterogeneity. Definition for index test positivity and reference standard were not found to be sources of heterogeneity. For ultrasound of the lungs (15 studies, 2410 participants, 1158 (48%) cases), the sensitivity ranged from 73% to 94% and the specificity ranged from 21% to 98%. The pooled sensitivity of ultrasound was 88.9% (95% CI 84.9 to 92.0), and the pooled specificity was 72.2% (95% CI 58.8 to 82.5). Definition for index test positivity and reference standard were not found to be sources of heterogeneity. Indirect comparisons of modalities evaluated across all 94 studies indicated that chest CT and ultrasound gave higher sensitivity estimates than X-ray (P = 0.0003 and P = 0.001, respectively). Chest CT and ultrasound gave similar sensitivities (P=0.42). All modalities had similar specificities (CT versus X-ray P = 0.36; CT versus ultrasound P = 0.32; X-ray versus ultrasound P = 0.89). Imaging in PCR-negative people who subsequently became positive For rate of positive imaging in individuals with initial RT-PCR negative results, we included 8 studies (7 CT, 1 ultrasound) with a total of 198 participants suspected of having COVID-19, all of whom had a final diagnosis of COVID-19. Most studies (7/8) evaluated CT. Of 177 participants with initially negative RT-PCR who had positive RT-PCR results on follow-up testing, 75.8% (95% CI 45.3 to 92.2) had positive CT findings. Imaging in asymptomatic PCR-positive people For imaging asymptomatic individuals, we included 10 studies (7 CT, 1 X-ray, 2 ultrasound) with a total of 3548 asymptomatic participants, of whom 364 (10%) had a final diagnosis of COVID-19. For chest CT (7 studies, 3134 participants, 315 (10%) cases), the pooled sensitivity was 55.7% (95% CI 35.4 to 74.3) and the pooled specificity was 91.1% (95% CI 82.6 to 95.7). AUTHORS' CONCLUSIONS: Chest CT and ultrasound of the lungs are sensitive and moderately specific in diagnosing COVID-19. Chest X-ray is moderately sensitive and moderately specific in diagnosing COVID-19. Thus, chest CT and ultrasound may have more utility for ruling out COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. The uncertainty resulting from high or unclear risk of bias and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results.

New clinical prediction model for early recognition of sepsis in adult primary care patients: a prospective diagnostic cohort study of development and external validation.

BACKGROUND: Recognising patients who need immediate hospital treatment for sepsis while simultaneously limiting unnecessary referrals is challenging for GPs. AIM: To develop and validate a sepsis prediction model for adult patients in primary care. DESIGN AND SETTING: This was a prospective cohort study in four out-of-hours primary care services in the Netherlands, conducted between June 2018 and March 2020. METHOD: Adult patients who were acutely ill and received home visits were included. A total of nine clinical variables were selected as candidate predictors, next to the biomarkers C-reactive protein, procalcitonin, and lactate. The primary endpoint was sepsis within 72 hours of inclusion, as established by an expert panel. Multivariable logistic regression with backwards selection was used to design an optimal model with continuous clinical variables. The added value of the biomarkers was evaluated. Subsequently, a simple model using single cut-off points of continuous variables was developed and externally validated in two emergency department populations. RESULTS: A total of 357 patients were included with a median age of 80 years (interquartile range 71-86), of which 151 (42%) were diagnosed with sepsis. A model based on a simple count of one point for each of six variables (aged >65 years; temperature >38°C; systolic blood pressure ≤110 mmHg; heart rate >110/min; saturation ≤95%; and altered mental status) had good discrimination and calibration (C-statistic of 0.80 [95% confidence interval = 0.75 to 0.84]; Brier score 0.175). Biomarkers did not improve the performance of the model and were therefore not included. The model was robust during external validation. CONCLUSION: Based on this study's GP out-of-hours population, a simple model can accurately predict sepsis in acutely ill adult patients using readily available clinical parameters.

Synovial-Based Tests Outperform Serum Markers to Rule Out Infection in Total Knee Arthroplasty and Total Hip Arthroplasty: A Systematic Review and Meta-Analysis.

BACKGROUND: Total knee arthroplasty (TKA) provides successful results in most patients. Periprosthetic joint infection (PJI) accounts for up to 25% of failed TKAs needing revision. In clinical practice, consensus in diagnostic strategy for excluding or diagnosing PJI is still lacking. In this systematic review and meta-analysis, we aim to provide a simplified data-driven diagnostic strategy for aseptic knee and hip revision surgeons to rule out PJI in the outpatient clinic phase. METHODS: A literature search in EMBASE, MEDLINE, PubMed, and Cochrane was conducted. Studies involving the diagnosis of PJI in patients with failed TKAs and total hip arthroplasties needing revision were identified. Only studies using the Musculoskeletal Infection Society criteria were included. Quality was assessed using MINORS criteria. Meta-analysis was performed for each diagnostic test identified in the included studies. Pooled estimates of diagnostic accuracy measures were calculated using a bivariate model and plotted in summary receiver-operator characteristic curves. Positive and negative predictive values were calculated in a hypothetical sample of patients with a given disease prevalence. RESULTS: Twenty-four studies met the inclusion criteria, describing a total of 2974 patients. Quality scores ranged from 13 to 19. Meta-analysis could be performed on 7 unique diagnostic tests. Highest pooled sensitivity and specificity were demonstrated for α-defensin with values of 86% and 96.6%, respectively. α-defensin and white blood cell count in synovial fluid demonstrate highest negative predictive value values. CONCLUSIONS: We recommend, in a clinical setting with low-intermediate prevalence of PJI, performing arthrocentesis and joint fluid analysis using α-defensin and/or white blood cell count before revision TKA and revision total hip arthroplasty surgery to rule out PJI.

Diagnostic value of routine chest tube tip culture in surgery for noninfectious lung disease.

BACKGROUND: Evaluation of the diagnostic value of routine chest tube tip culture for detection of postoperative infection after surgery for noninfectious lung disease. METHODS: Included subjects were patients who underwent lung surgery between January 1st 2013 and January 1st 2018 in University Medical Centre Utrecht and of whom a chest tube tip was cultured. Postoperative outcomes included pneumonia, surgical site infection, and empyema within 30 days after surgery. Univariable analysis for diagnostic accuracy of chest tube tip culture results predicting these postoperative outcomes was performed, as well as multivariable analysis using penalized firth logistic regression. RESULTS: Patients developed one or more postoperative infections in 42 out of 210 (20%) lung surgeries. Pneumonia, surgical site infection, and empyema were found in 36 (17%), 8 (4%), and 2 (1%) cases respectively. Chest tube tip culture had a sensitivity of 31%, a specificity of 83%, a positive predictive value of 32%, and a negative predictive value of 83% for postoperative infections. In the subgroup of patients who did not have evidence of postoperative infection at the time of chest tube removal, the drain tip culture's positive and negative predictive value changed to 18% and 92% respectively. Adding additional variables to chest tube tip culture in a prediction model resulting in only limited improvement in diagnostic performance. CONCLUSIONS: We found insufficient diagnostic performance to support the practice of routine chest tube tip culture after surgery for noninfectious lung disease. Therefore, routine chest tube tip culture is not advisable and should be omitted to unburden the healthcare process and prevent low value care together with extra costs.

Effectiveness of contact tracing apps for SARS-CoV-2: a rapid systematic review.

OBJECTIVE: To systematically review evidence on effectiveness of contact tracing apps (CTAs) for SARS-CoV-2 on epidemiological and clinical outcomes. DESIGN: Rapid systematic review. DATA SOURCES: EMBASE (OVID), MEDLINE (PubMed), BioRxiv and MedRxiv were searched up to 28 October 2020. STUDY SELECTION: Studies, both empirical and model-based, assessing effect of CTAs for SARS-CoV-2 on reproduction number (R), total number of infections, hospitalisation rate, mortality rate, and other epidemiologically and clinically relevant outcomes, were eligible for inclusion. DATA EXTRACTION: Empirical and model-based studies were critically appraised using separate checklists. Data on type of study (ie, empirical or model-based), sample size, (simulated) time horizon, study population, CTA type (and associated interventions), comparator and outcomes assessed, were extracted. The most important findings were extracted and narratively summarised. Specifically for model-based studies, characteristics and values of important model parameters were collected. RESULTS: 2140 studies were identified, of which 17 studies (2 empirical, 15 model-based studies) were eligible and included in this review. Both empirical studies were observational (non-randomised) studies and at high risk of bias, most importantly due to risk of confounding. Risk of bias of model-based studies was considered low for 12 out of 15 studies. Most studies demonstrated beneficial effects of CTAs on R, total number of infections and mortality rate. No studies assessed effect on hospitalisation. Effect size was dependent on model parameters values used, but in general, a beneficial effect was observed at CTA adoption rates of 20% or higher. CONCLUSIONS: CTAs have the potential to be effective in reducing SARS-CoV-2 related epidemiological and clinical outcomes, though effect size depends on other model parameters (eg, proportion of asymptomatic individuals, or testing delays), and interventions after CTA notification. Methodologically sound comparative empirical studies on effectiveness of CTAs are required to confirm findings from model-based studies.

Efficacy of bone-substitute materials use in immediate dental implant placement: A systematic review and meta-analysis.

OBJECTIVE: To assess the efficacy of using a bone substitute material (BSM) in the fixture-socket gap in patients undergoing tooth extraction and immediate implant placement. MATERIALS AND METHODS: MEDLINE, EMBASE, and CENTRAL databases were searched for randomized controlled trials (RCTs). RCTs were screened for eligibility, and data were extracted by two authors independently. Risk of bias (ROB) was assessed using Cochrane's ROB tool 2.0. Primary outcomes were implant failure, overall complications, and soft-tissue esthetics. Secondary outcomes were vertical buccal bone resorption, vertical interproximal bone resorption, horizontal buccal bone resorption, and mid-buccal mucosal recession. Meta-analysis was performed using random-effects model with generic inverse variance weighing. GRADE was used to grade the certainty of the evidence. RESULTS: After screening 19 544 potentially eligible references, 20 RCTs were included in this review, with a total of 848 patients (916 sites). Most included RCTs were deemed of some concerns (53%) or at low (38%) risk of bias, except for overall complications (high ROB). Implant failure did not differ significantly RR = 0.92 (confidence intervals [CI] 0.34 to 2.46) between using a BSM compared with not using a BSM (NoBSM). BSM use resulted in less horizontal buccal bone resorption (MD = -0.52 mm [95% CI -0.74 to -0.30]), a higher esthetic score (MD = 1.49 [95% CI 0.46 to 2.53]), but also more complications (RR = 3.50 [95% CI 1.11 to 11.1] compared with NoBSM. Too few trials compared types of BSMs against each other to allow for pooled analyses. The certainty of the evidence was considered moderate for all outcomes except implant failure (low), overall complications (very low), and vertical interproximal bone resorption (very low). CONCLUSION: BSM use during immediate implant placement reduces horizontal buccal bone resorption and improves the periimplant soft-tissue esthetics. Although BSM use increases the risk of predominantly minor complications.

Thoracic imaging tests for the diagnosis of COVID-19.

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.

Accuracy of continuous glucose monitoring in preterm infants: a systematic review and meta-analysis.

BACKGROUND AND OBJECTIVES: Continuous glucose monitoring (CGM) could be a valuable instrument for measurement of glucose concentration in preterm neonate. We undertook a systematic review and meta-analysis to compare the diagnostic accuracy of CGM devices to intermittent blood glucose evaluation methods for the detection of hypoglycaemic or hypoglycaemic events in preterm infants. DATA SOURCES: A structured electronic database search was performed for studies that assessed the accuracy of CGM against any intermittent blood glucose testing methods in detecting episodes of altered glycaemia in preterm infants. No restrictions were used. Three review authors screened records and included studies. DATA EXTRACTION: Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. From individual patient data (IPD), sensitivity and specificity were determined using predefined thresholds. The mean absolute relative difference (MARD) of the studied CGM devices was assessed and if those satisfied the accuracy requirements (EN ISO 15197). IPD datasets were meta-analysed using a logistic mixed-effects model. A bivariate model was used to estimate the summary receiver operating characteristic curve (ROC) curve and extract the area under the curve (AUC). The overall level of certainty of the evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation. RESULTS: Among 4481 records, 11 were included. IPD datasets were obtained for five studies. Only two of the studies showed an MARD lower than 10%, with none of the five CGM devices studied satisfying the European Union (EU) ISO 15197 requirements. Pooled sensitivity and specificity of CGM devices for hypoglycaemia were 0.39 and 0.99, whereas for hyperglycaemia were 0.87 and 0.99, respectively. The AUC was 0.70 and 0.86, respectively. The certainty of the evidence was considered as low to moderate. Limitations primarily related to the lack of representative population, reference standard and CGM device. CONCLUSIONS: CGM devices demonstrated low sensitivity for detecting hypoglycaemia in preterm infants, however, provided high accuracy for detection of hyperglycaemia. PROSPERO REGISTRATION NUMBER: CRD42020152248.

Thoracic imaging tests for the diagnosis of COVID-19.

BACKGROUND: The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Early research showed thoracic (chest) imaging to be sensitive but not specific in the diagnosis of coronavirus disease 2019 (COVID-19). However, this is a rapidly developing field and these findings need to be re-evaluated in the light of new research. This is the first update of this 'living systematic review'. This update focuses on people suspected of having COVID-19 and excludes studies with only confirmed COVID-19 participants. 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 22 June 2020. We did not apply any language restrictions. SELECTION CRITERIA: We included studies of all designs that recruited participants of any age group suspected to have COVID-19, and which reported estimates of test accuracy, or provided data from which estimates could be computed. When studies used a variety of reference standards, we retained the classification of participants as COVID-19 positive or negative as used in the study. DATA COLLECTION AND ANALYSIS: We screened studies, extracted data, and assessed the risk of bias and applicability concerns using the QUADAS-2 domain-list independently, in duplicate. We categorised included studies into three groups based on classification of index test results: studies that reported specific criteria for index test positivity (group 1); studies that did not report specific criteria, but had the test reader(s) explicitly classify the imaging test result as either COVID-19 positive or negative (group 2); and studies that reported an overview of index test findings, without explicitly classifying the imaging test as either COVID-19 positive or negative (group 3). We presented the results of estimated sensitivity and specificity using paired forest plots, and summarised in tables. We used a bivariate meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs). MAIN RESULTS: We included 34 studies: 30 were cross-sectional studies with 8491 participants suspected of COVID-19, of which 4575 (54%) had a final diagnosis of COVID-19; four were case-control studies with 848 cases and controls in total, of which 464 (55%) had a final diagnosis of COVID-19. Chest CT was evaluated in 31 studies (8014 participants, 4224 (53%) cases), chest X-ray in three studies (1243 participants, 784 (63%) cases), and ultrasound of the lungs in one study (100 participants, 31 (31%) cases). Twenty-six per cent (9/34) of all studies were available only as preprints. Nineteen studies were conducted in Asia, 10 in Europe, four in North America and one in Australia. Sixteen studies included only adults, 15 studies included both adults and children and one included only children. Two studies did not report the ages of participants. Twenty-four studies included inpatients, four studies included outpatients, while the remaining six studies were conducted in unclear settings. The majority of included studies had a high or unclear risk of bias with respect to participant selection, index test, reference standard, and participant flow. For chest CT in suspected COVID-19 participants (31 studies, 8014 participants, 4224 (53%) cases) the sensitivity ranged from 57.4% to 100%, and specificity ranged from 0% to 96.0%. The pooled sensitivity of chest CT in suspected COVID-19 participants was 89.9% (95% CI 85.7 to 92.9) and the pooled specificity was 61.1% (95% CI 42.3 to 77.1). Sensitivity analyses showed that when the studies from China were excluded, the studies from other countries demonstrated higher specificity compared to the overall included studies. When studies that did not classify index tests as positive or negative for COVID-19 (group 3) were excluded, the remaining studies (groups 1 and 2) demonstrated higher specificity compared to the overall included studies. Sensitivity analyses limited to cross-sectional studies, or studies where at least two reverse transcriptase polymerase chain reaction (RT-PCR) tests were conducted if the first was negative, did not substantively alter the accuracy estimates. We did not identify publication status as a source of heterogeneity. For chest X-ray in suspected COVID-19 participants (3 studies, 1243 participants, 784 (63%) cases) the sensitivity ranged from 56.9% to 89.0% and specificity from 11.1% to 88.9%. The sensitivity and specificity of ultrasound of the lungs in suspected COVID-19 participants (1 study, 100 participants, 31 (31%) cases) were 96.8% and 62.3%, respectively. We could not perform a meta-analysis for chest X-ray or ultrasound due to the limited number of included studies. AUTHORS' CONCLUSIONS: Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19 in suspected patients, meaning that CT may have limited capability in differentiating SARS-CoV-2 infection from other causes of respiratory illness. However, we are limited in our confidence in these results due to the poor study quality and the heterogeneity of included studies. Because of limited data, accuracy estimates of chest X-ray and ultrasound of the lungs for the diagnosis of suspected COVID-19 cases should be carefully interpreted. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest on the same participant population, and implement improved reporting practices. Planned updates of this review will aim to: increase precision around the accuracy estimates for chest CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X-rays and ultrasound; and obtain data to further fulfil secondary objectives (e.g. 'threshold' effects, comparing accuracy estimates across different imaging modalities) to inform the utility of imaging along different diagnostic pathways.

Thoracic imaging tests for the diagnosis of COVID-19.

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.

Development of a clinical prediction rule for sepsis in primary care: protocol for the TeSD-IT study.

BACKGROUND: Early recognition and treatment of sepsis is crucial to prevent detrimental outcomes. General practitioners (GPs) are often the first healthcare providers to encounter seriously ill patients. The aim of this study is to assess the value of clinical information and additional tests to develop a clinical prediction rule to support early diagnosis and management of sepsis by GPs. METHODS: We will perform a diagnostic study in the setting of out-of-hours home visits in four GP cooperatives in the Netherlands. Acutely ill adult patients suspected of a serious infection will be screened for eligibility by the GP. The following candidate predictors will be prospectively recorded: (1) age, (2) body temperature, (3) systolic blood pressure, (4) heart rate, (5) respiratory rate, (6) peripheral oxygen saturation, (7) mental status, (8) history of rigors, and (9) rate of progression. After clinical assessment by the GP, blood samples will be collected in all patients to measure C-reactive protein, lactate, and procalcitonin. All patients will receive care as usual. The primary outcome is the presence or absence of sepsis within 72 h after inclusion, according to an expert panel. The need for hospital treatment for any indication will be assessed by the expert panel as a secondary outcome. Multivariable logistic regression will be used to design an optimal prediction model first and subsequently derive a simplified clinical prediction rule that enhances feasibility of using the model in daily clinical practice. Bootstrapping will be performed for internal validation of both the optimal model and simplified prediction rule. Performance of both models will be compared to existing clinical prediction rules for sepsis. DISCUSSION: This study will enable us to develop a clinical prediction rule for the recognition of sepsis in a high-risk primary care setting to aid in the decision which patients have to be immediately referred to a hospital and who can be safely treated at home. As clinical signs and blood samples will be obtained prospectively, near-complete data will be available for analyses. External validation will be needed before implementation in routine care and to determine in which pre-hospital settings care can be improved using the prediction rule. TRIAL REGISTRATION: The study is registered in the Netherlands Trial Registry (registration number NTR7026).

Data sources and methods used to determine pretest probabilities in a cohort of Cochrane diagnostic test accuracy reviews.

BACKGROUND: A pretest probability must be selected to calculate data to help clinicians, guideline boards and policy makers interpret diagnostic accuracy parameters. When multiple analyses for the same target condition are compared, identical pretest probabilities might be selected to facilitate the comparison. Some pretest probabilities may lead to exaggerations of the patient harms or benefits, and guidance on how and why to select a specific pretest probability is minimally described. Therefore, the aim of this study was to assess the data sources and methods used in Cochrane diagnostic test accuracy (DTA) reviews for determining pretest probabilities to facilitate the interpretation of DTA parameters. A secondary aim was to assess the use of identical pretest probabilities to compare multiple meta-analyses within the same target condition. METHODS: Cochrane DTA reviews presenting at least one meta-analytic estimate of the sensitivity and/or specificity as a primary analysis published between 2008 and January 2018 were included. Study selection and data extraction were performed by one author and checked by other authors. Observed data sources (e.g. studies in the review, or external sources) and methods to select pretest probabilities (e.g. median) were categorized. RESULTS: Fifty-nine DTA reviews were included, comprising of 308 meta-analyses. A pretest probability was used in 148 analyses. Authors used included studies in the DTA review, external sources, and author consensus as data sources for the pretest probability. Measures of central tendency with or without a measure of dispersion were used to determine the pretest probabilities, with the median most commonly used. Thirty-two target conditions had at least one identical pretest probability for all of the meta-analyses within their target condition. About half of the used identical pretest probabilities were inside the prevalence ranges from all analyses within a target condition. CONCLUSIONS: Multiple sources and methods were used to determine (identical) pretest probabilities in Cochrane DTA reviews. Indirectness and severity of downstream consequences may influence the acceptability of the certainty in calculated data with pretest probabilities. Consider: whether to present normalized frequencies, the influence of pretest probabilities on normalized frequencies, and whether to use identical pretest probabilities for meta-analyses in a target condition.

Decision analytic modeling was useful to assess the impact of a prediction model on health outcomes before a randomized trial.

OBJECTIVE: To demonstrate how decision analytic models (DAMs) can be used to quantify impact of using a (diagnostic or prognostic) prediction model in clinical practice and provide general guidance on how to perform such assessments. STUDY DESIGN AND SETTING: A DAM was developed to assess the impact of using the HEART score for predicting major adverse cardiac events (MACE). Impact on patient health outcomes and health care costs was assessed in scenarios by varying compliance with and informed deviation (ID) (using additional clinical knowledge) from HEART score management recommendations. Probabilistic sensitivity analysis was used to assess estimated impact robustness. RESULTS: Impact of using the HEART score on health outcomes and health care costs was influenced by an interplay of compliance with and ID from HEART score management recommendations. Compliance of 50% (with 0% ID) resulted in increased missed MACE and costs compared with usual care. Any compliance combined with at least 50% ID reduced both costs and missed MACE. Other scenarios yielded a reduction in missed MACE at higher costs. CONCLUSION: Decision analytic modeling is a useful approach to assess impact of using a prediction model in practice on health outcomes and health care costs. This approach is recommended before conducting an impact trial to improve its design and conduct.

Clinical biomarker innovation: when is it worthwhile?

Background Choosing which biomarker tests to select for further research and development is not only a matter of diagnostic accuracy, but also of the clinical and monetary benefits downstream. Early health economic modeling provides tools to assess the potential effects of biomarker innovation and support decision-making. Methods We applied early health economic modeling to the case of diagnosing primary aldosteronism in patients with resistant hypertension. We simulated a cohort of patients using a Markov cohort state-transition model. Using the headroom method, we compared the currently used aldosterone-to-renin ratio to a hypothetical new test with perfect diagnostic properties to determine the headroom based on quality-adjusted life-years (QALYs) and costs, followed by threshold analyses to determine the minimal diagnostic accuracy for a cost-effective product. Results Our model indicated that a perfect diagnostic test would yield 0.027 QALYs and increase costs by €43 per patient. At a cost-effectiveness threshold of €20,000 per QALY, the maximum price for this perfect test to be cost-effective is €498 (95% confidence interval [CI]: €275-€808). The value of the perfect test was most strongly influenced by the sensitivity of the current biomarker test. Threshold analysis showed the novel test needs a sensitivity of at least 0.9 and a specificity of at least 0.7 to be cost-effective. Conclusions Our model-based approach evaluated the added value of a clinical biomarker innovation, prior to extensive investment in development, clinical studies and implementation. We conclude that early health economic modeling can be a valuable tool when prioritizing biomarker innovations in the laboratory.

Forcing dichotomous disease classification from reference standards leads to bias in diagnostic accuracy estimates: A simulation study.

OBJECTIVES: The objective of this study was to study the impact of ignoring uncertainty by forcing dichotomous classification (presence or absence) of the target disease on estimates of diagnostic accuracy of an index test. STUDY DESIGN AND SETTING: We evaluated the bias in estimated index test accuracy when forcing an expert panel to make a dichotomous target disease classification for each individual. Data for various scenarios with expert panels were simulated by varying the number and accuracy of "component reference tests" available to the expert panel, index test sensitivity and specificity, and target disease prevalence. RESULTS: Index test accuracy estimates are likely to be biased when there is uncertainty surrounding the presence or absence of the target disease. Direction and amount of bias depend on the number and accuracy of component reference tests, target disease prevalence, and the true values of index test sensitivity and specificity. CONCLUSION: In this simulation, forcing expert panels to make a dichotomous decision on target disease classification in the presence of uncertainty leads to biased estimates of index test accuracy. Empirical studies are needed to demonstrate whether this bias can be reduced by assigning a probability of target disease presence for each individual, or using advanced statistical methods to account for uncertainty in target disease classification.