Disease Misclassification Research Articles

The burden of diagnostic error in dentistry: A study on periodontal disease misclassification

BackgroundPeriodontal disease constitutes a widely prevalent category of non-communicable diseases and ranks among the top 10 causes of disability worldwide. Little however is known about diagnostic errors in dentistry. In this work, by retrospectively deploying an electronic health record (EHR)-based trigger tool, followed by gold standard manual review, we provide epidemiological estimates on the rate of diagnostic misclassification in dentistry through a periodontal use case. MethodsAn EHR-based trigger tool (a retrospective record review instrument that uses a list of triggers (or clues), i.e., data elements within the health record, to alert reviewers to the potential presence of a wrong diagnosis) was developed, tested and run against the EHR at the two participating sites to flag all cases having a potential misdiagnosis. All cases flagged as potentially misdiagnosed underwent extensive manual reviews by two calibrated domain experts. A subset of the non-flagged cases was also manually reviewed. ResultsA total of 2,262 patient charts met the study's inclusion criteria. Of these, the algorithm flagged 1,124 cases as potentially misclassified and 1,138 cases as potentially correctly diagnosed. When the algorithm identified a case as potentially misclassified, compared to the diagnosis assigned by the gold standard, the kappa statistic was 0.01. However, for cases the algorithm marked as potentially correctly diagnosed, the review against the gold standard showed a kappa statistic of 0.9, indicating near perfect agreement. The observed proportion of diagnostic misclassification was 32 %. There was no significant difference by clinic or provider characteristics. ConclusionOur work revealed that about a third of periodontal cases are misclassified. Diagnostic errors have been reported to happen more frequently than other types of errors, and to be more preventable. Benchmarking diagnostic quality is a first step. Subsequent research endeavor will delve into comprehending the factors that contribute to diagnostic errors in dentistry and instituting measures to prevent them. Clinical SignificanceThis study sheds light on the significance of diagnostic excellence in the delivery of dental care, and highlights the potential role of technology in aiding diagnostic decision-making at the point of care.

The Disease Loophole: Index Terms and Their Role in Disease Misclassification.

The definitions of disease proffered by philosophers and medical actors typically require that a state of ill health be linked to some known bodily dysfunction before it is classified as a disease. I argue that such definitions of disease are not fully implementable in current medical discourse and practice. Adhering to the definitions would require that medical actors keep close track of the current state of knowledge on the causes and mechanisms of particular illnesses. Yet, unaddressed problems in medical terminology can make this difficult to do. I show that unrecognized misuse of "heterogeneous," "biomarker," and other important health terms-which I call index terms-can misrepresent the current empirical evidence on illness pathophysiology, such that unvalidated illness constructs become mistaken for diseases. Thus, implementing common definitions of disease would require closing this "loophole" in medical discourse. I offer a simple rule that, if followed, could help do just that.

The effect of disease misclassification on the ability to detect a gene-environment interaction: implications of the specificity of case definitions for research on Gulf War illness

BackgroundSince 1997, research on Gulf War illness (GWI) has predominantly used 3 case definitions—the original Research definition, the CDC definition, and modifications of the Kansas definition—but they have not been compared against an objective standard.MethodsAll 3 case definitions were measured in the U.S. Military Health Survey by a computer-assisted telephone interview in a random sample (n = 6,497) of the 1991 deployed U.S. military force. The interview asked whether participants had heard nerve agent alarms during the conflict. A random subsample (n = 1,698) provided DNA for genotyping the PON1 Q192R polymorphism.ResultsThe CDC and the Modified Kansas definition without exclusions were satisfied by 41.7% and 39.0% of the deployed force, respectively, and were highly overlapping. The Research definition, a subset of the others, was satisfied by 13.6%. The majority of veterans meeting CDC and Modified Kansas endorsed fewer and milder symptoms; whereas, those meeting Research endorsed more symptoms of greater severity. The group meeting Research was more highly enriched with the PON1 192R risk allele than those meeting CDC and Modified Kansas, and Research had twice the power to detect the previously described gene-environment interaction between hearing alarms and RR homozygosity (adjusted relative excess risk due to interaction [aRERI] = 7.69; 95% CI 2.71–19.13) than CDC (aRERI = 2.92; 95% CI 0.96–6.38) or Modified Kansas without exclusions (aRERI = 3.84; 95% CI 1.30–8.52) or with exclusions (aRERI = 3.42; 95% CI 1.20–7.56). The lower power of CDC and Modified Kansas relative to Research was due to greater false-positive disease misclassification from lower diagnostic specificity.ConclusionsThe original Research case definition had greater statistical power to detect a genetic predisposition to GWI. Its greater specificity favors its use in hypothesis-driven research; whereas, the greater sensitivity of the others favor their use in clinical screening for application of future diagnostic biomarkers and clinical care.

Misclassification of females and males in cardiovascular magnetic resonance parametric mapping: the importance of sex-specific normal ranges for diagnosis of health vs. disease.

Cardiovascular magnetic resonance parametric mapping enables non-invasive quantitative myocardial tissue characterization. Human myocardium has normal ranges of T1 and T2 values, deviation from which may indicate disease or change in physiology. Normal myocardial T1 and T2 values are affected by biological sex. Consequently, normal ranges created with insufficient numbers of each sex may result in sampling biases, misclassification of healthy values vs. disease, and even misdiagnoses. In this study, we investigated the impact of using male normal ranges for classifying female cases as normal or abnormal (and vice versa). One hundred and forty-two healthy volunteers (male and female) were scanned on two Siemens 3T MR systems, providing averaged global myocardial T1 and T2 values on a per-subject basis. The Monte Carlo method was used to generate simulated normal ranges from these values to estimate the statistical accuracy of classifying healthy female or male cases correctly as 'normal' when using sex-specific vs. mixed-sex normal ranges. The normal male and female T1- and T2-mapping values were significantly different by sex, after adjusting for age and heart rate. Using 15 healthy volunteers who are not sex specific to establish a normal range resulted in a typical misclassification of up to 36% of healthy females and 37% of healthy males as having abnormal T1 values and up to 16% of healthy females and 12% of healthy males as having abnormal T2 values. This paper highlights the potential adverse impact on diagnostic accuracy that can occur when local normal ranges contain insufficient numbers of both sexes. Sex-specific reference ranges should thus be routinely adopted in clinical practice.

HLA-B*27 is associated with CNO in a European cohort

ObjectivesTo determine the influence of HLA-B27 positivity on risk of developing chronic nonbacterial osteomyelitis (CNO).MethodsHLA-B*27 genotype was assessed in 3 European CNO populations and compared with local control populations (572 cases, 33,256 controls). Regional or whole-body MRI was performed at diagnosis and follow-up in all cases which reduces the risk of disease misclassification. Genotyping was performed using either next generation DNA sequencing or PCR based molecular typing. Statistical analysis used Fisher’s exact test with Bonferroni correction and a fixed effects model for meta-analysis of odds ratios.ResultsHLA-B*27 frequency was higher in all 3 populations compared with local controls (combined odds ratio (OR) = 2.2, p-value = 3 × 10–11). This association was much stronger in male compared with female cases (OR = 1.99, corrected p-value = 0.015). However, the HLA-B*27 status was not statistically significantly associated with co-occurrence of psoriasis, arthritis or inflammatory bowel disease.ConclusionCarriage of HLA-B*27 is associated with greater risk of developing CNO, particularly in male cases.

The feasibility of existing JADAS10 cut-off values in clinical practice: a study of data from The Finnish Rheumatology Quality Register

BackgroundThe ten-joint juvenile arthritis disease activity score (JADAS10) is designed to measure the level of disease activity in non-systemic juvenile idiopathic arthritis by providing a single numeric score. The clinical JADAS10 (cJADAS10) is a modification of the JADAS10 that excludes erythrocyte sedimentation rate (ESR). Three different sets of JADAS10/cJADAS10 cut-offs for disease activity states have been published, i.e., the Backström, Consolaro, and Trincianti cut-offs. The objective of this study was to investigate the performance of existing JADAS10 cut-offs in real-life settings using patient data from The Finnish Rheumatology Quality Register (FinRheuma).MethodsData were collected from the FinRheuma register. The proportion of patients with an active joint count (AJC) above zero when classified as being in clinically inactive disease (CID) or low disease activity (LDA) groups according to existing JADAS10/cJADAS10 cut-off levels were analyzed.ResultsA significantly larger proportion of the patients classified as being in CID had an AJC > 0 when using the JADAS10/cJADAS10 cut-offs by Trincianti et al. compared to those for the other cut-offs. In the LDA group, a significantly larger proportion of the polyarticular patients (35%/29%) had an AJC of two when Trincianti JADAS10/cJADAS10 cut-offs were used compared with when Backström (11%/10%) and Consolaro (7%/3%) JADAS10/cJADAS10 cut-offs were used.ConclusionsWe found the cut-offs proposed by Consolaro et al. to be the most feasible, since these cut-off levels for CID do not result in the misclassification of active disease as remission, and the proportion of patients with AJC > 1 in the LDA group is lowest using these cut-offs.

Commentary: Rethinking 10-2 visual fields in early diagnosis of glaucoma for a glided glaucoma practice: The right choice to pick up a feeble noise?

Commentary: Rethinking 10-2 visual fields in early diagnosis of glaucoma for a glided glaucoma practice: The right choice to pick up a feeble noise?

Single-Night Diagnosis of Sleep Apnea Contributes to Inconsistent Cardiovascular Outcome Findings

Single-night disease misclassification of OSA due to night-to-night variability may contribute to inconsistent findings in OSA trials. Does multinight quantification of OSA severity provide more precise estimates of associations with incident hypertension? A total of 3,831 participants without hypertension at baseline were included in simulation analyses. Included participants had≥ 28days of nightly apnea-hypopnea index (AHI) recordings via an under-mattress sensor and≥ 3 separate BP measurements over a 3-month baseline period followed by≥ 3 separate BP measurements 6 to 9months postbaseline. Incident hypertension was defined as a mean systolic BP≥ 140mmHg or a mean diastolic BP≥ 90mmHg. Simulated trials (1,000) were performed, using bootstrap methods to investigate the effect of variable numbers of nights (x= 1-56 per participant) to quantify AHI and the ability to detect associations between OSA and incident hypertension via logistic regression adjusted for age, sex, and BMI. Participants were middle-aged (mean ± SD, 52 ± 12 y), mostly men (91%), and overweight (BMI, 28 ± 5kg/m2). Single-night quantification of OSA failed to detect an association with hypertension risk in 42%of simulated trials (α= 0.05). Conversely, 100%of trials detected an association when AHI was quantified over≥ 28 nights. Point estimates of hypertension risk were also 50%higher and uncertainty was 5 times lower during multinight vssingle-night simulation trials. Multinight monitoring of OSA allows for better estimates of hypertension risk and potentially other adverse health outcomes associated with OSA. These findings have important implications for clinical care and OSA trial design.

Atopic Dermatitis in Latin America: A Roadmap to Address Data Collection, Knowledge Gaps, and Challenges.

Atopic dermatitis (AD) is a systemic, multifactorial disease that causes significant morbidity and health care burden in Latin America (LA). Data on AD are scarce in LA. Lack of disease registries and non-standardized study methodologies, coupled with region-specific genetic, immunological, and environmental factors, hamper data collection. A panel of LA experts in AD was given a series of relevant questions to address before a conference. Each narrative was discussed and edited through numerous rounds of deliberation until achieving consensus. Identified knowledge gaps in AD research were updated prevalence, adult-disease epidemiology, local phenotypes and endotypes, severe-disease prevalence, specialist distribution, and AD public health policy. Underlying reasons for these gaps include limited funding for AD research, from epidemiology and public policy to clinical and translational studies. Regional heterogeneity requires that complex interactions between race, ethnicity, and environmental factors be further studied. Informed awareness, education, and decision making should be encouraged.

The Impact of an Electronic Health Record Intervention on Spirometry Completion in Patients with Chronic Obstructive Pulmonary Disease

Spirometry is necessary to diagnose chronic obstructive pulmonary disease (COPD), yet a large proportion of patients are diagnosed and treated without having received testing. This study explored whether the effects of interventions using the electronic health record (EHR) to target patients diagnosed with COPD without confirmatory spirometry impacted the incidence rates of spirometry referrals and completions. This retrospective before and after study assessed the impact of provider-facing clinical decision support that identified patients who had a diagnosis of COPD but had not received spirometry. Spirometry referrals, completions, and results were ascertained 1.5 years prior to and 1.5 years after the interventions were initiated. Inhaler prescriptions by class were also tallied. There were 10,949 unique patients with a diagnosis of COPD who were eligible for inclusion. 4,895 patients (44.7%) were excluded because they had completed spirometry prior to the cohort start dates. The pre-intervention cohort consisted of 2,622 patients, while the post-intervention cohort had 3,392. Spirometry referral rates pre-intervention were 20.2% compared to 31.6% post-intervention (p < 0.001). Spirometry completion rates rose from 13.2% pre-intervention to 19.3% afterwards (p < 0.001). 61.7% (585 of 948) had no evidence of airflow obstruction. After excluding patients with a diagnosis of asthma, 25.8% (126 of 488) patients who had no evidence of airflow obstruction had prescriptions for long-acting bronchodilators or inhaled steroids. A concerted EHR intervention modestly increased spirometry referral and completion rates in patients with a diagnosis of COPD without prior spirometry and decreased misclassification of disease.

Prevalence and burden of COPD misclassification in the Canadian Longitudinal Study on Aging (CLSA)

IntroductionTo examine the prevalence of chronic obstructive pulmonary disease (COPD) misclassification and the associated burden of symptoms, healthcare utilisation and physical performance status in the Canadian general population. This information...

Night-to-night variability of sleep apnea detected by cyclic variation of heart rate during long-term continuous ECG monitoring.

BackgroundSleep apnea is common in patients with cardiovascular disease and is a factor that worsens prognosis. Holter 24‐h ECG screening for sleep apnea is beneficial in the care of these patients, but due to high night‐to‐night variability of sleep apnea, it can lead to misdiagnosis and misclassification of disease severity.MethodsTo investigate the long‐term dynamic behavior of sleep apnea, seven‐day ECGs recorded with a patch ECG recorder in 120 patients were analyzed for the cyclic variation of heart rate (CVHR) during sleep periods as determined by a built‐in three‐axis accelerometer.ResultsThe frequency of CVHR (Fcv) showed considerable night‐to‐night variability (coefficient of variance, 66 ± 35%), which was consistent with the night‐to‐night variability in apnea‐hypopnea index and oxygen desaturation index reported in earlier studies. In patients with presumed moderate‐to‐severe sleep apnea (Fcv > 15 cph at least one night), it was missed on 62% of nights, and on at least one night in 88% of patients. The CV of Fcv was negatively correlated with the average of Fcv, suggesting that patients with mild sleep apnea show greater night‐to‐night variability and would benefit from long‐term assessment. The average Fcv was higher in the supine position, but the night‐to‐night variability was not explained by the night‐to‐night variability of time spent in the supine position.ConclusionsCVHR analysis of long‐term ambulatory ECG recordings is useful for improving the reliability of screening for sleep apnea without placing an extra burden on patients with cardiovascular disease and their care.

Cervical cancer screening outcomes in Zambia, 2010–19: a cohort study

Globally, cervical cancer is the fourth leading cause of cancer-related death among women. Poor uptake of screening services contributes to the high mortality. We aimed to examine screening frequency, predictors of screening results, and patterns of sensitisation strategies by age group in a large, programmatic cohort. We did a cohort study including 11 government health facilities in Lusaka, Zambia, in which we reviewed routine programmatic data collected through the Cervical Cancer Prevention Program in Zambia (CCPPZ). Participants who underwent cervical cancer screening in one of the participating study sites were considered for study inclusion if they had a screening result. Follow-up was accomplished per national guidelines. We did descriptive analyses and mixed-effects logistic regression for cervical cancer screening results allowing random effects at the individual and clinic level. Between Jan 1, 2010, and July 31, 2019, we included 183 165 women with 204 225 results for visual inspection with acetic acid and digital cervicography (VIAC) in the analysis. Of all those screened, 21 326 (10·4%) were VIAC-positive, of whom 16 244 (76·2%) received treatment. Of 204 225 screenings, 92 838 (45·5%) were in women who were HIV-negative, 76 607 (37·5%) were in women who were HIV-positive, and 34 780 (17·0%) had an unknown HIV status. Screening frequency increased 65·7% between 2010 and 2019 with most appointments being first-time screenings (n=158 940 [77·8%]). Women with HIV were more likely to test VIAC-positive than women who were HIV-negative (adjusted odds ratio 3·60, 95% CI 2·14-6·08). Younger women (≤29 years) with HIV had the highest predictive probability (18·6%, 95% CI 14·2-22·9) of screening positive. CCPPZ has effectively increased women's engagement in screening since its inception in 2006. Customised sensitisation strategies relevant to different age groups could increase uptake and adherence to screening. The high proportion of screen positivity in women younger than 20 years with HIV requires further consideration. Our data are not able to discern if women with HIV have earlier disease onset or whether this difference reflects misclassification of disease in an age group with a higher sexually transmitted infection prevalence. These data inform scale-up efforts required to achieve WHO elimination targets. US President's Emergency Plan for AIDS Relief.

A Prospective Ultrasound Study of Plasma Polychlorinated Biphenyl Concentrations and Incidence of Uterine Leiomyomata.

Uterine leiomyomata, or fibroids, are hormone-dependent neoplasms of the myometrium that can cause severe gynecologic morbidity. In previous studies, incidence of these lesions has been positively associated with exposure to polychlorinated biphenyls (PCBs), a class of persistent endocrine-disrupting chemicals. However, previous studies have been retrospective in design and none has used ultrasound to reduce disease misclassification. The Study of Environment, Lifestyle, and Fibroids is a prospective cohort of 1,693 reproductive-aged Black women residing in Detroit, Michigan (enrolled during 2010-2012). At baseline and every 20 months for 5 years, women completed questionnaires, provided blood samples, and underwent transvaginal ultrasound to detect incident fibroids. We analyzed 754 baseline plasma samples for concentrations of 24 PCB congeners using a case-cohort study design. We used multivariable Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals for the association between plasma PCB concentrations and ultrasound-detected fibroid incidence over a 5-year period. We observed little association between PCB congener concentrations and fibroid incidence. The HR for a one-standard deviation increase in log-transformed total PCBs was 0.94 (95% CI = 0.78, 1.1). The PCB congener with the largest effect estimate was PCB 187 (HR for a one-standard deviation increase in log-transformed exposure = 0.88, 95% CI = 0.73, 1.1). Associations did not seem to vary strongly across PCB groupings based on hormonal activity. In this cohort of reproductive-aged Black women, plasma PCB concentrations typical of the contemporary general population were not appreciably associated with higher risk of fibroids.

Boosted-DEPICT: an effective maize disease categorization framework using deep clustering

Clustering of plant disease from digital images is an arduous task due to its dynamic nature and change of appearance under different environmental conditions. In most cases, the image captured in the real-time scenario is subjected to added noise, distortion, poor lighting conditions, and other potential factors that results in poor model performance during the process of discriminating between normal and disease-affected samples. It eventually maximizes the margin of the error rate, thereby leading to misclassification of disease of different varieties of plants in the database with other categories. This paper presents an effective deep clustering-based plant disease categorization algorithm, Boosted-Deep Embedded Regularized Clustering (DEPICT). This model integrates the convolutional autoencoder model with locality-preserving constraints and group sparsity into the network, which improves the embedded learning representation of the images. The PlantVillage and PDD image databases are accessed to develop this model for maize crop. The images are segmented by eliminating the background, cropped, augmented before model training. The performance of the system is evaluated by clustering accuracy and normalized mutual information. The proposed Boosted-DEPICT exhibits better performance, attains promising results with an accuracy of 97.73% and 91.25% on PV and PDD datasets, and outperforms state-of-the-art deep clustering algorithms. This system could be further enhanced by automating the entire process and transforming it into a mobile application for real-time analysis to gain instant results from any region.

Health screening results of Cubans settling in Texas, USA, 2010-2015: A cross-sectional analysis.

BackgroundProtecting the health of refugees and other migrant populations in the United States is key to ensuring successful resettlement. Therefore, to identify and address health concerns early, the US Centers for Disease Control and Prevention (CDC) recommends a domestic medical examination (screening for infectious and noninfectious diseases/conditions) shortly after arrival in the US. However, because refugee/migrant populations often have differing health patterns from one another and the US population, the collection and analysis of health information is key to developing population-specific clinical guidelines to guide the care of resettled individuals. Yet little is known regarding the health status of Cubans resettling in the US. Among the tens of thousands of Cuban migrants who have resettled in the US, some applied as refugees in Cuba, some applied for parole (a term used to indicate temporary US admission status for urgent humanitarian reasons or reasons of public benefit under US immigration law) in Cuba, and others applied for parole status after crossing the border. These groups were eligible for US government benefits to help them resettle, including a domestic medical examination. We reviewed health differences found in these examinations of those who were determined to be refugees or parolees in Cuba and those who were given parole status after arrival.Methods and findingsWe conducted a retrospective cross-sectional analysis of the Texas Department of State Health Services database. Cubans who arrived from 2010 to 2015 and received a domestic medical examination in Texas were included. Those granted refugee/parolee status in Cuba were listed in federal databases for US-bound refugees/parolees; those who were paroled after arrival were not listed. Overall, 2,189 (20%) obtained either refugee or parolee status in Cuba, and 8,709 (80%) received parolee status after arrival. Approximately 62% of those who received parolee status after arrival at the border were male, compared with 49% of those who obtained prior refugee/parolee status in Cuba. Approximately one-half (45%) of those paroled after arrival were 19–34 years old (versus 26% among those who obtained refugee/parolee status in Cuba). Separate models were created for each screening indicator as the outcome, with entry route as the main exposure variable. Crude and adjusted prevalence ratios were estimated using PROC GENMOD procedures in SAS 9.4. Individuals paroled after arrival were less likely to screen positive for parasitic infections (9.6% versus 12.2%; adjusted prevalence ratio: 0.79, 0.71–0.88) and elevated blood lead levels (children ≤16 years old, 5.2% versus 12.3%; adjusted prevalence ratio: 0.42, 0.28–0.63). Limitations include potential disease misclassification, missing clinical information, and cross-sectional nature.ConclusionsWithin-country variations in health status are often not examined in refugee populations, yet they are critical to understand granular health trends. Results suggests that the health profiles of Cuban Americans in Texas differed by entry route. This information could assist in developing targeted screenings and health interventions.

Development and Evaluation of a Prediction Model for Ascertaining Rheumatic Heart Disease Status in Administrative Data.

BackgroundPrevious research has raised substantial concerns regarding the validity of the International Statistical Classification of Diseases and Related Health Problems (ICD) codes (ICD-10 I05–I09) for rheumatic heart disease (RHD) due to likely misclassification of non-rheumatic valvular disease (non-rheumatic VHD) as RHD. There is currently no validated, quantitative approach for reliable case ascertainment of RHD in administrative hospital data.MethodsA comprehensive dataset of validated Australian RHD cases was compiled and linked to inpatient hospital records with an RHD ICD code (2000–2018, n=7555). A prediction model was developed based on a generalized linear mixed model structure considering an extensive range of demographic and clinical variables. It was validated internally using randomly selected cross-validation samples and externally. Conditional optimal probability cutpoints were calculated, maximising discrimination separately for high-risk versus low-risk populations.ResultsThe proposed model reduced the false-positive rate (FPR) from acute rheumatic fever (ARF) cases misclassified as RHD from 0.59 to 0.27; similarly for non-rheumatic VHD from 0.77 to 0.22. Overall, the model achieved strong discriminant capacity (AUC: 0.93) and maintained a similar robust performance during external validation (AUC: 0.88). It can also be used when only basic demographic and diagnosis data are available.ConclusionThis paper is the first to show that not only misclassification of non-rheumatic VHD but also of ARF as RHD yields substantial FPRs. Both sources of bias can be successfully addressed with the proposed model which provides an effective solution for reliable RHD case ascertainment from hospital data for epidemiological disease monitoring and policy evaluation.

Disease misclassification in electronic healthcare database studies: Deriving validity indices-A contribution from the ADVANCE project.

There is a strong and continuously growing interest in using large electronic healthcare databases to study health outcomes and the effects of pharmaceutical products. However, concerns regarding disease misclassification (i.e. classification errors of the disease status) and its impact on the study results are legitimate. Validation is therefore increasingly recognized as an essential component of database research. In this work, we elucidate the interrelations between the true prevalence of a disease in a database population (i.e. prevalence assuming no disease misclassification), the observed prevalence subject to disease misclassification, and the most common validity indices: sensitivity, specificity, positive and negative predictive value. Based on this, we obtained analytical expressions to derive all the validity indices and true prevalence from the observed prevalence and any combination of two other parameters. The analytical expressions can be used for various purposes. Most notably, they can be used to obtain an estimate of the observed prevalence adjusted for outcome misclassification from any combination of two validity indices and to derive validity indices from each other which would otherwise be difficult to obtain. To allow researchers to easily use the analytical expressions, we additionally developed a user-friendly and freely available web-application.

Recurrence of FSGS after Kidney Transplantation in Adults.

FSGS recurrence after kidney transplantation is a major risk factor for graft loss. However, the natural history, clinical predictors, and response to treatment remain unclear because of small sample sizes and poor generalizability of single-center studies, and disease misclassification in registry-based studies. We therefore aimed to determine the incidence, predictors, and treatment response of recurrent FSGS in a large cohort of kidney transplant recipients. The Post-Transplant Glomerular Disease (TANGO) project is an observational, multicenter, international cohort study that aims to investigate glomerular disease recurrence post-transplantation. Transplant recipients were screened for the diagnosis of idiopathic FSGS between 2005 and 2015 and details were recorded about the transplant, clinical outcomes, treatments, and other risk factors. Among 11,742 kidney transplant recipients screened for FSGS, 176 had a diagnosis of idiopathic FSGS and were included. FSGS recurred in 57 patients (32%; 95% confidence interval [95% CI], 25% to 39%) and 39% of them lost their graft over a median of 5 (interquartile range, 3.0-8.1) years. Multivariable Cox regression revealed a higher risk for recurrence with older age at native kidney disease onset (hazard ratio [HR], 1.37 per decade; 95% CI, 1.09 to 1.56). Other predictors were white race (HR, 2.14; 95% CI, 1.08 to 4.22), body mass index at transplant (HR, 0.89 per kg/m2; 95% CI, 0.83 to 0.95), and native kidney nephrectomies (HR, 2.76; 95% CI, 1.16 to 6.57). Plasmapheresis and rituximab were the most frequent treatments (81%). Partial or complete remission occurred in 57% of patients and was associated with better graft survival. Idiopathic FSGS recurs post-transplant in one third of cases and is associated with a five-fold higher risk of graft loss. Response to treatment is associated with significantly better outcomes but is achieved in only half of the cases.

Misclassification of Mitral Valve Disease and Rate of Surgical Repair in The Society of Thoracic Surgeons Database

Surgical repair of primary mitral regurgitation (MR) is considered an indicator of quality performance. Therefore, accurate data reporting is critical for quality assessment. During an institutional quality review, MR etiology could not be determined in 40% of operations in The Society of Thoracic Surgeons database entries, and therefore our true repair rate could not be reliably ascertained. Therefore, we reviewed all source documents and echocardiograms to assess our true disease etiology and repair rate. Source records and echocardiograms of all operations performed in a single health care system for a 1-year period were reviewed by an experienced mitral valve surgeon, an echocardiographic core laboratory, and a data manager. Disease etiology and operation were compared with data previously entered in the database by post hoc chart abstraction. In all, 314 isolated mitral valve operations were performed. The MR was originally classified as primary, 163 (52%); secondary, 22 (7%); rheumatic, 37 (12%); endocarditis, 24 (8%); other, 33 (10%); and unknown, 35 (11%). Reported repair rate for primary MR was 142 of 163 (87.1%). After review, etiology was determined to be primary, 177 (56%); secondary, 33 (11%); rheumatic, 61 (20%); endocarditis, 25 (8%); and others, 18 (5%)-resulting in a change of classification in 99 of 314 patients (31.5%) and a true repair rate for primary MR of 165 of 177 (93.2%). Source document and imaging review of mitral valve surgery revealed significant discordance with post hoc chart abstraction methods. A more detailed data entry methodology is necessary to accurately report the true disease etiology and repair rates for primary MR.