Penalized Model-Based Unsupervised Phenomapping Unravels Distinctive HFrEF Phenotypes With Improved Outcomes Discrimination From Sacubitril/Valsartan Treatment Independent of MAGGIC Score

Background The angiotensin receptor-neprilysin inhibitor (LCZ696) has emerged as a promising pharmacological intervention against renin-angiotensin system inhibitor in reduced ejection fraction heart failure (HFrEF). Whether the therapeutic benefits may vary among heterogeneous HFrEF subgroups remains unknown. Methods and Results This study comprised a pooled 2-center analysis including 1103 patients with symptomatic HFrEF with LCZ696 use and another 1103 independent HFrEF control cohort (with renin-angiotensin system inhibitor use) matched for age, sex, left ventricular ejection fraction, and comorbidity conditions. Three main distinct phenogroup clusterings were identified from unsupervised machine learning using 29 clinical variables: phenogroup 1 (youngest, relatively lower diabetes prevalence, highest glomerular filtration rate with largest left ventricular size and left ventricular wall stress); phenogroup 2 (oldest, lean, highest diabetes and vascular diseases prevalence, lowest highest glomerular filtration rate with smallest left ventricular size and mass), and phenogroup 3 (lowest clinical comorbidity with largest left ventricular mass and highest hypertrophy prevalence). During the median 1.74-year follow-up, phenogroup assignment provided improved prognostic discrimination beyond Meta-Analysis Global Group in Chronic Heart Failure risk score risk score (all net reclassification index P<0.05) with overall good calibrations. While phenogroup 1 showed overall best clinical outcomes, phenogroup 2 demonstrated highest cardiovascular death and worst renal end point, with phenogroup 3 having the highest all-cause death rate and HF hospitalization among groups, respectively. These findings were broadly consistent when compared with the renin-angiotensin system inhibitor control as reference group. Conclusions Phenomapping provided novel insights on unique characteristics and cardiac features among patients with HFrEF with sacubitril/valsartan treatment. These findings further showed potentiality in identifying potential sacubitril/valsartan responders and nonresponders with improved outcome discrimination among patients with HFrEF beyond clinical scoring.

Adjusting for verification bias in diagnostic accuracy measures when comparing multiple screening tests - an application to the IP1-PROSTAGRAM study

Introduction

Novel screening tests used to detect a target condition are compared against either a reference standard or other existing screening methods. However, as it is not always possible to apply the reference standard on the whole population under study, verification bias is introduced. Statistical methods exist to adjust estimates to account for this bias. We extend common methods to adjust for verification bias when multiple tests are compared to a reference standard using data from a prospective double blind screening study for prostate cancer.

Methods

Begg and Greenes method and multiple imputation are extended to include the results of multiple screening tests which determine condition verification status. These two methods are compared to the complete case analysis using the IP1-PROSTAGRAM study data. IP1-PROSTAGRAM used a paired-cohort double-blind design to evaluate the use of imaging as alternative tests to screen for prostate cancer, compared to a blood test called prostate specific antigen (PSA). Participants with positive imaging (index) and/or PSA (control) underwent a prostate biopsy (reference standard).

Results

When comparing complete case results to Begg and Greenes and methods of multiple imputation there is a statistically significant increase in the specificity estimates for all screening tests. Sensitivity estimates remained similar across the methods, with completely overlapping 95% confidence intervals. Negative predictive value (NPV) estimates were higher when adjusting for verification bias, compared to complete case analysis, even though the 95% confidence intervals overlap. Positive predictive value (PPV) estimates were similar across all methods.

Conclusion

Statistical methods are required to adjust for verification bias in accuracy estimates of screening tests. Expanding Begg and Greenes method to include multiple screening tests can be computationally intensive, hence multiple imputation is recommended, especially as it can be modified for low prevalence of the target condition.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01481-w.

Statistical methods for evaluating the fine needle aspiration cytology procedure in breast cancer diagnosis

Background

Statistical issues present while evaluating a diagnostic procedure for breast cancer are non rare but often ignored, leading to biased results. We aimed to evaluate the diagnostic accuracy of the fine needle aspiration cytology(FNAC), a minimally invasive and rapid technique potentially used as a rule-in or rule-out test, handling its statistical issues: suspect test results and verification bias.

Methods

We applied different statistical methods to handle suspect results by defining conditional estimates. When considering a partial verification bias, Begg and Greenes method and multivariate imputation by chained equations were applied, however, and a Bayesian approach with respect to each gold standard was used when considering a differential verification bias. At last, we extended the Begg and Greenes method to be applied conditionally on the suspect results.

Results

The specificity of the FNAC test above 94%, was always higher than its sensitivity regardless of the proposed method. All positive likelihood ratios were higher than 10, with variations among methods. The positive and negative yields were high, defining precise discriminating properties of the test.

Conclusion

The FNAC test is more likely to be used as a rule-in test for diagnosing breast cancer. Our results contributed in advancing our knowledge regarding the performance of FNAC test and the methods to be applied for its evaluation.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12874-022-01506-y).

Correcting for partial verification bias in diagnostic accuracy studies: A tutorial using R

Diagnostic tests play a crucial role in medical care. Thus any new diagnostic tests must undergo a thorough evaluation. New diagnostic tests are evaluated in comparison with the respective gold standard tests. The performance of binary diagnostic tests is quantified by accuracy measures, with sensitivity and specificity being the most important measures. In any diagnostic accuracy study, the estimates of these measures are often biased owing to selective verification of the patients, which is referred to as partial verification bias. Several methods for correcting partial verification bias are available depending on the scale of the index test, target outcome, and missing data mechanism. However, these are not easily accessible to the researchers due to the complexity of the methods. This article aims to provide a brief overview of the methods available to correct for partial verification bias involving a binary diagnostic test and provide a practical tutorial on how to implement the methods using the statistical programming language R.

Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review

OBJECTIVE

To provide an overview and critical appraisal of prediction models for bronchopulmonary dysplasia (BPD) in preterm infants.

METHODS

We searched PubMed, Embase, and the Cochrane Library to identify relevant studies (up to November 2021). We included studies that reported prediction model development and/or validation of BPD in preterm infants born at ≤32 weeks and/or ≤1,500 g birth weight. We extracted the data independently based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). We assessed risk of bias and applicability independently using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

RESULTS

Twenty-one prediction models from 13 studies reporting on model development and 21 models from 10 studies reporting on external validation were included. Oxygen dependency at 36 weeks' postmenstrual age was the most frequently reported outcome in both development studies (71%) and validation studies (81%). The most frequently used predictors in the models were birth weight (67%), gestational age (62%), and sex (52%). Nearly all included studies had high risk of bias, most often due to inadequate analysis. Small sample sizes and insufficient event patients were common in both study types. Missing data were often not reported or were discarded. Most studies reported on the models' discrimination, while calibration was seldom assessed (development, 19%; validation, 10%). Internal validation was lacking in 69% of development studies.

CONCLUSION

The included studies had many methodological shortcomings. Future work should focus on following the recommended approaches for developing and validating BPD prediction models.

The External Validity of Prediction Models for the Diagnosis of Obstructive Coronary Artery Disease in Patients With Stable Chest Pain: Insights From the PROMISE Trial

OBJECTIVES

This study sought to externally validate prediction models for the presence of obstructive coronary artery disease (CAD).

BACKGROUND

A better assessment of the probability of CAD may improve the identification of patients who benefit from noninvasive testing.

METHODS

Stable chest pain patients from the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial with computed tomography angiography (CTA) or invasive coronary angiography (ICA) were included. The authors assumed that patients with CTA showing 0% stenosis and a coronary artery calcium (CAC) score of 0 were free of obstructive CAD (≥50% stenosis) on ICA, and they multiply imputed missing ICA results based on clinical variables and CTA results. Predicted CAD probabilities were calculated using published coefficients for 3 models: basic model (age, sex, chest pain type), clinical model (basic model + diabetes, hypertension, dyslipidemia, and smoking), and clinical + CAC score model. The authors assessed discrimination and calibration, and compared published effects with observed predictor effects.

RESULTS

In 3,468 patients (1,805 women; mean 60 years of age; 779 [23%] with obstructive CAD on CTA), the models demonstrated moderate-good discrimination, with C-statistics of 0.69 (95% confidence interval [CI]: 0.67 to 0.72), 0.72 (95% CI: 0.69 to 0.74), and 0.86 (95% CI: 0.85 to 0.88) for the basic, clinical, and clinical + CAC score models, respectively. Calibration was satisfactory although typical chest pain and diabetes were less predictive and CAC score was more predictive than was suggested by the models. Among the 31% of patients for whom the clinical model predicted a low (≤10%) probability of CAD, actual prevalence was 7%; among the 48% for whom the clinical + CAC score model predicted a low probability the observed prevalence was 2%. In 2 sensitivity analyses excluding imputed data, similar results were obtained using CTA as the outcome, whereas in those who underwent ICA the models significantly underestimated CAD probability.

CONCLUSIONS

Existing clinical prediction models can identify patients with a low probability of obstructive CAD. Obstructive CAD on ICA was imputed for 61% of patients; hence, further validation is necessary.

Partial verification bias and incorporation bias affected accuracy estimates of diagnostic studies for biomarkers that were part of an existing composite gold standard

OBJECTIVE

To investigate how choice of gold standard biases estimates of sensitivity and specificity in studies reassessing the diagnostic accuracy of biomarkers that are already part of a lifetime composite gold standard (CGS).

STUDY DESIGN AND SETTING

We performed a simulation study based on the real-life example of the biomarker "protein 14-3-3" used for diagnosing Creutzfeldt-Jakob disease. Three different types of gold standard were compared: perfect gold standard "autopsy" (available in a small fraction only; prone to partial verification bias), lifetime CGS (including the biomarker under investigation; prone to incorporation bias), and "best available" gold standard (autopsy if available, otherwise CGS).

RESULTS

Sensitivity was unbiased when comparing 14-3-3 with autopsy but overestimated when using CGS or "best available" gold standard. Specificity of 14-3-3 was underestimated in scenarios comparing 14-3-3 with autopsy (up to 24%). In contrast, overestimation (up to 20%) was observed for specificity compared with CGS; this could be reduced to 0-10% when using the "best available" gold standard.

CONCLUSION

Choice of gold standard affects considerably estimates of diagnostic accuracy. Using the "best available" gold standard (autopsy where available, otherwise CGS) leads to valid estimates of specificity, whereas sensitivity is estimated best when tested against autopsy alone.

Anticipating missing reference standard data when planning diagnostic accuracy studies

Results obtained using a reference standard may be missing for some participants in diagnostic accuracy studies. This paper looks at methods for dealing with such missing data when designing or conducting a prospective diagnostic accuracy study

Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data

Modern statistical methods using incomplete data have been increasingly applied in a wide variety of substantive problems. Similarly, receiver operating characteristic (ROC) analysis, a method used in evaluating diagnostic tests or biomarkers in medical research, has also been increasingly popular problem in both its development and application. While missing-data methods have been applied in ROC analysis, the impact of model mis-specification and/or assumptions (e.g. missing at random) underlying the missing data has not been thoroughly studied. In this work, we study the performance of multiple imputation (MI) inference in ROC analysis. Particularly, we investigate parametric and non-parametric techniques for MI inference under common missingness mechanisms. Depending on the coherency of the imputation model with the underlying data generation mechanism, our results show that MI generally leads to well-calibrated inferences under ignorable missingness mechanisms.

A hybrid Bayesian hierarchical model combining cohort and case-control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias

To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities, and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented.

A Bayesian Approach for the Cox Proportional Hazards Model with Covariates Subject to Detection Limit

The research on biomarkers has been limited in its effectiveness because biomarker levels can only be measured within the thresholds of assays and laboratory instruments, a challenge referred to as a detection limit (DL) problem. In this paper, we propose a Bayesian approach to the Cox proportional hazards model with explanatory variables subject to lower, upper, or interval DLs. We demonstrate that by formulating the time-to-event outcome using the Poisson density with counting process notation, implementing the proposed approach in the OpenBUGS and JAGS is straightforward. We have conducted extensive simulations to compare the proposed Bayesian approach to the other four commonly used methods and to evaluate its robustness with respect to the distribution assumption of the biomarkers. The proposed Bayesian approach and other methods were applied to an acute lung injury study, in which a panel of cytokine biomarkers was studied for the biomarkers’ association with ventilation-free survival.

Diagnostic and prognostic prediction models

Risk prediction models can be used to estimate the probability of either having (diagnostic model) or developing a particular disease or outcome (prognostic model). In clinical practice, these models are used to inform patients and guide therapeutic management. Examples from the field of venous thrombo-embolism (VTE) include the Wells rule for patients suspected of deep venous thrombosis and pulmonary embolism, and more recently prediction rules to estimate the risk of recurrence after a first episode of unprovoked VTE. In this paper, the three phases that are recommended before a prediction model may be used in daily practice are described: development, validation, and impact assessment. In the development phase, the focus is on model development commonly using a multivariable logistic (diagnostic) or survival (prognostic) regression analysis. The performance of the developed model is expressed by discrimination, calibration and (re-) classification. In the validation phase, the developed model is tested in a new set of patients using these same performance measures. This is important, as model performance is commonly poorer in a new set of patients, e.g. due to case-mix or domain differences. Finally, in the impact phase the ability of a prediction model to actually guide patient management is evaluated. Whereas in the development and validation phase single cohort designs are preferred, this last phase asks for comparative designs, ideally randomized designs; therapeutic management and outcomes after using the prediction model is compared to a control group not using the model (e.g. usual care).

The effect of age and recent influenza vaccination history on the immunogenicity and efficacy of 2009-10 seasonal trivalent inactivated influenza vaccination in children

BACKGROUND

There is some evidence that annual vaccination of trivalent inactivated influenza vaccine (TIV) may lead to reduced vaccine immunogenicity but evidence is lacking on whether vaccine efficacy is affected by prior vaccination history. The efficacy of one dose of TIV in children 6-8 y of age against influenza B is uncertain. We examined whether immunogenicity and efficacy of influenza vaccination in school-age children varied by age and past vaccination history.

METHODS AND FINDINGS

We conducted a randomized controlled trial of 2009-10 TIV. Influenza vaccination history in the two preceding years was recorded. Immunogenicity was assessed by comparison of HI titers before and one month after receipt of TIV/placebo. Subjects were followed up for 11 months with symptom diaries, and respiratory specimens were collected during acute respiratory illnesses to permit confirmation of influenza virus infections. We found that previous vaccination was associated with reduced antibody responses to TIV against seasonal A(H1N1) and A(H3N2) particularly in children 9-17 y of age, but increased antibody responses to the same lineage of influenza B virus in children 6-8 y of age. Serological responses to the influenza A vaccine viruses were high regardless of vaccination history. One dose of TIV appeared to be efficacious against confirmed influenza B in children 6-8 y of age regardless of vaccination history.

CONCLUSIONS

Prior vaccination was associated with lower antibody titer rises following vaccination against seasonal influenza A vaccine viruses, but higher responses to influenza B among individuals primed with viruses from the same lineage in preceding years. In a year in which influenza B virus predominated, no impact of prior vaccination history was observed on vaccine efficacy against influenza B. The strains that circulated in the year of study did not allow us to study the effect of prior vaccination on vaccine efficacy against influenza A.

Use of multiple imputation method to improve estimation of missing baseline serum creatinine in acute kidney injury research

BACKGROUND AND OBJECTIVES

Baseline creatinine (BCr) is frequently missing in AKI studies. Common surrogate estimates can misclassify AKI and adversely affect the study of related outcomes. This study examined whether multiple imputation improved accuracy of estimating missing BCr beyond current recommendations to apply assumed estimated GFR (eGFR) of 75 ml/min per 1.73 m(2) (eGFR 75).

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

From 41,114 unique adult admissions (13,003 with and 28,111 without BCr data) at Vanderbilt University Hospital between 2006 and 2008, a propensity score model was developed to predict likelihood of missing BCr. Propensity scoring identified 6502 patients with highest likelihood of missing BCr among 13,003 patients with known BCr to simulate a "missing" data scenario while preserving actual reference BCr. Within this cohort (n=6502), the ability of various multiple-imputation approaches to estimate BCr and classify AKI were compared with that of eGFR 75.

RESULTS

All multiple-imputation methods except the basic one more closely approximated actual BCr than did eGFR 75. Total AKI misclassification was lower with multiple imputation (full multiple imputation + serum creatinine) (9.0%) than with eGFR 75 (12.3%; P<0.001). Improvements in misclassification were greater in patients with impaired kidney function (full multiple imputation + serum creatinine) (15.3%) versus eGFR 75 (40.5%; P<0.001). Multiple imputation improved specificity and positive predictive value for detecting AKI at the expense of modestly decreasing sensitivity relative to eGFR 75.

CONCLUSIONS

Multiple imputation can improve accuracy in estimating missing BCr and reduce misclassification of AKI beyond currently proposed methods.

A Bayesian Approach for Generalized Linear Models with Explanatory Biomarker Measurement Variables Subject to Detection Limit - an Application to Acute Lung Injury

Biomarkers have the potential to improve our understanding of disease diagnosis and prognosis. Biomarker levels that fall below the assay detection limits (DLs), however, compromise the application of biomarkers in research and practice. Most existing methods to handle non-detects focus on a scenario in which the response variable is subject to detection limit; only a few methods consider explanatory variables when dealing with DLs. We propose a Bayesian approach for generalized linear models with explanatory variables subject to lower, upper, or interval DLs. In simulation studies, we compared the proposed Bayesian approach to four commonly used methods in a logistic regression model with explanatory variable measurements subject to DL. We also applied the Bayesian approach and other four methods in a real study, in which a panel of cytokine biomarkers was studied for their association with acute lung injury (ALI). We found that IL8 was associated with a moderate increase in risk for ALI in the model based on the proposed Bayesian approach.

Adjusting for partial verification or workup bias in meta-analyses of diagnostic accuracy studies

A key requirement in the design of diagnostic accuracy studies is that all study participants receive both the test under evaluation and the reference standard test. For a variety of practical and ethical reasons, sometimes only a proportion of patients receive the reference standard, which can bias the accuracy estimates. Numerous methods have been described for correcting this partial verification bias or workup bias in individual studies. In this article, the authors describe a Bayesian method for obtaining adjusted results from a diagnostic meta-analysis when partial verification or workup bias is present in a subset of the primary studies. The method corrects for verification bias without having to exclude primary studies with verification bias, thus preserving the main advantages of a meta-analysis: increased precision and better generalizability. The results of this method are compared with the existing methods for dealing with verification bias in diagnostic meta-analyses. For illustration, the authors use empirical data from a systematic review of studies of the accuracy of the immunohistochemistry test for diagnosis of human epidermal growth factor receptor 2 status in breast cancer patients.

How to deal with double partial verification when evaluating two index tests in relation to a reference test?

Research into the diagnostic accuracy of clinical tests is often hampered by single or double partial verification mechanisms, that is, not all patients have their disease status verified by a reference test, neither do all patients receive all tests under evaluation (index tests). We show methods that reduce verification bias introduced when omitting data from partially tested patients. Adjustment techniques are well established when there are no missing index tests and when the reference test is 'missing at random'. However, in practice, index tests tend to be omitted, and the choice of applying a reference test may depend on unobserved variables related to disease status, that is, verification may be missing not at random (MNAR). We study double partial verification in a clinical example from reproductive medicine in which we analyse the diagnostic values of the chlamydia antibody test and the hysterosalpingography in relation to a diagnostic laparoscopy. First, we plot all possible combinations of sensitivity and specificity of both index tests in two test ignorance regions. Then, we construct models in which we impose different assumptions for the verification process. We allow for missing index tests, study the influence of patient characteristics and study the accuracy estimates if an MNAR mechanism would operate. It is shown that data on tests used in the diagnostic process of the same population are preferably studied jointly and that the influence of an MNAR verification process was limited in a clinical study where more than half of the patients did not have the reference test.

The utility of pitch elevation in the evaluation of oropharyngeal Dysphagia: preliminary findings

PURPOSE

To evaluate the utility of a pitch elevation task in the assessment of oropharyngeal dysphagia.

METHOD

This study was a pilot prospective cohort study including 40 consecutive patients (16 male and 24 female) who were referred by their physician for a swallowing evaluation. Patients were evaluated with a noninstrumental clinical examination and a videofluoroscopic swallow study, and participated in a pitch elevation task during videofluoroscopic image acquisition. Relationships between pitch elevation measurements (acoustic and perceptual) and swallow parameters (penetration/aspiration and residue) were investigated.

RESULTS

Results of this pilot study revealed that both maximum fundamental frequency (F(0)) and perceptual evaluation of pitch elevation independently significantly predicted Penetration-Aspiration Scale scores for thin liquid swallows (p = .01 and .03, respectively). Vocal range (average pitch to falsetto) was not sensitive in predicting likelihood of oropharyngeal dysphagia.

CONCLUSIONS

Findings indicate that reduced pitch elevation can be indicative of reduced airway protection and swallowing impairment in some dysphagia patients and may be a useful supplement to dysphagia screening and diagnosis. Further investigation is warranted to determine the optimal utility of this procedure for different diagnostic categories of patients.

Bayesian longitudinal plateau model of adult grip strength

OBJECTIVES

This article illustrates the use of applied Bayesian statistical methods in modeling the trajectory of adult grip strength and in evaluating potential risk factors that may influence that trajectory.

METHODS

The data consist of from 1 to 11 repeated grip strength measurements from each of 498 men and 533 women age 18-96 years in the Fels Longitudinal Study (Roche AF. 1992. Growth, maturation and body composition: the Fels longitudinal study 1929-1991. Cambridge: Cambridge University Press). In this analysis, the Bayesian framework was particularly useful for fitting a nonlinear mixed effects plateau model with two unknown change points and for the joint modeling of a time-varying covariate. Multiple imputation (MI) was used to handle missing values with posterior inferences appropriately adjusted to account for between-imputation variability.

RESULTS

On average, men and women attain peak grip strength at the same age (36 years), women begin to decline in grip strength sooner (age 50 years for women and 56 years for men), and men lose grip strength at a faster rate relative to their peak; there is an increasing secular trend in peak grip strength that is not attributable to concurrent secular trends in body size, and the grip strength trajectory varies with birth weight (men only), smoking (men only), alcohol consumption (men and women), and sports activity (women only).

CONCLUSIONS

Longitudinal data analysis requires handling not only serial correlation but often also time-varying covariates, missing data, and unknown change points. Bayesian methods, combined with MI, are useful in handling these issues.

Factors Associated With Outcome in Heart Failure With Preserved Ejection Fraction

Background—

The determinants of prognosis in patients with heart failure and preserved ejection fraction (HF-PEF) are poorly documented.

Methods and Results—

We evaluated data from 4128 patients in the I-PRESERVE trial (Irbesartan in Heart Failure with Preserved Ejection Fraction Study). Multivariable Cox regression models were developed using 58 baseline demographic, clinical, and biological variables to model the primary outcome of all-cause mortality or cardiovascular hospitalization (1505 events), all-cause mortality (881 events), and HF death or hospitalization (716 events). Log N-terminal pro–B-type natriuretic peptide, age, diabetes mellitus, and previous hospitalization for HF were the most powerful factors associated with the primary outcome and with the HF composite. For all-cause mortality, log N-terminal pro–B-type natriuretic peptide, age, diabetes mellitus, and left ventricular EF were the strongest independent factors. Other independent factors associated with poor outcome included quality of life, a history of chronic obstructive lung disease, log neutrophil count, heart rate, and estimated glomerular filtration rate. The models accurately stratified the actual 3-year rate of outcomes from 8.1% to 59.9% (primary outcome) 2.7% to 36.5% (all-cause mortality), and 2.1% to 38.9% (HF composite) for the lowest to highest septiles of predicted risks.

Conclusions—

In a large sample of elderly patients with HF and preserved EF enrolled in I-Preserve, simple clinical, demographic, and biological variables were associated with outcome and identified subgroups at very high and very low risk of events.

Clinical Trial Registration—

URL: http://www.clinicaltrials.gov . Unique identifier: NCT00095238.

Are patient characteristics associated with the accuracy of hysterosalpingography in diagnosing tubal pathology? An individual patient data meta-analysis

BACKGROUND

Conventional meta-analysis has estimated the sensitivity and specificity of hysterosalpingography (HSG) to be 65% and 83%. The impact of patient characteristics on the accuracy of HSG is unknown. The aim of this study was to assess by individual patient data meta-analysis whether the accuracy of HSG is associated with different patient characteristics.

METHODS

We approached authors of primary studies reporting on the accuracy of HSG using findings at laparoscopy as the reference. We assessed whether patient characteristics such as female age, duration of subfertility and a clinical history without risk factors for tubal pathology were associated with the accuracy of HSG, using a random intercept logistic regression model.

RESULTS

We acquired data of seven primary studies containing data of 4521 women. Pooled sensitivity and specificity of HSG were 53% and 87% for any tubal pathology and 46% and 95% for bilateral tubal pathology. In women without risk factors, the sensitivity of HSG was 38% for any tubal pathology, compared with 61% in women with risk factors (P = 0.005). For bilateral tubal pathology, these rates were 13% versus 47% (P = 0.01). For bilateral tubal pathology, the sensitivity of HSG decreased with age [factor 0.93 per year (P = 0.05)]. The specificity of HSG was very stable across all subgroups.

CONCLUSIONS

The accuracy of HSG in detecting tubal pathology was similar in all subgroups, except for women without risk factors in whom sensitivity was lower, possibly due to false-positive results at laparoscopy. HSG is a useful tubal patency screening test for all infertile couples.