Two-stage method to remove population- and individual-level outliers from longitudinal data in a primary care database

Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system

BACKGROUND

Several strategies for identifying biologically implausible values in longitudinal anthropometric data have recently been proposed, but the suitability of these strategies for large population datasets needs to be better understood. This study evaluated the impact of removing population outliers and the additional value of identifying and removing longitudinal outliers on the trajectories of length/height and weight and on the prevalence of child growth indicators in a large longitudinal dataset of child growth data.

METHODS

Length/height and weight measurements of children aged 0 to 59 months from the Brazilian Food and Nutrition Surveillance System were analyzed. Population outliers were identified using z-scores from the World Health Organization (WHO) growth charts. After identifying and removing population outliers, residuals from linear mixed-effects models were used to flag longitudinal outliers. The following cutoffs for residuals were tested to flag those: -3/+3, -4/+4, -5/+5, -6/+6. The selected child growth indicators included length/height-for-age z-scores and weight-for-age z-scores, classified according to the WHO charts.

RESULTS

The dataset included 50,154,738 records from 10,775,496 children. Boys and girls had 5.74% and 5.31% of length/height and 5.19% and 4.74% of weight values flagged as population outliers, respectively. After removing those, the percentage of longitudinal outliers varied from 0.02% (<-6/>+6) to 1.47% (<-3/>+3) for length/height and from 0.07 to 1.44% for weight in boys. In girls, the percentage of longitudinal outliers varied from 0.01 to 1.50% for length/height and from 0.08 to 1.45% for weight. The initial removal of population outliers played the most substantial role in the growth trajectories as it was the first step in the cleaning process, while the additional removal of longitudinal outliers had lower influence on those, regardless of the cutoff adopted. The prevalence of the selected indicators were also affected by both population and longitudinal (to a lesser extent) outliers.

CONCLUSIONS

Although both population and longitudinal outliers can detect biologically implausible values in child growth data, removing population outliers seemed more relevant in this large administrative dataset, especially in calculating summary statistics. However, both types of outliers need to be identified and removed for the proper evaluation of trajectories.

Addition of progesterone to feminizing gender-affirming hormone therapy in transgender individuals for breast development: a randomized controlled trial

BACKGROUND

Feminizing gender-affirming hormone therapy (GAHT) for transgender individuals traditionally includes estradiol and androgen deprivation. Research has demonstrated that breast size as a result of GAHT in transgender women is often limited. Therefore, transgender women often choose to undergo breast augmentation surgery. Progesterone is important for breast development in cisgender women during puberty. A potential role for progesterone in breast development in transgender women has not been investigated in a randomized controlled experimental set-up. The primary objective of this study is to explore the effects on breast volume of addition of oral progesterone to GAHT with estradiol in transgender women after vaginoplasty or orchiectomy. Secondary objectives include assessment of safety, satisfaction, mood, sleep and sexual pleasure.

METHODS

This is a non-blinded, non-placebo, randomized controlled trial using a factorial design in adult transgender individuals assigned male sex at birth who have undergone GAHT for at least one year and underwent vaginoplasty or orchiectomy. The study design allows for rapid assessment of potential synergistic effects of various dose combinations of estradiol and progesterone on breast volume change: Ninety participants will be randomized into six groups of 15 subjects each, receiving either the baseline dose of estradiol, the baseline dose of estradiol and progesterone 200 mg daily, the baseline dose of estradiol and progesterone 400 mg daily, twice the baseline dose of estradiol, twice the baseline dose of estradiol and progesterone 200 mg daily or twice the baseline dose of estradiol and progesterone 400 mg daily, all for a duration of 12 months. The main study parameters include changes in breast volume as determined by 3D measurements. Participants will be followed-up with laboratory testing including serum progesterone concentrations as well as surveys for satisfaction, mood, sleep quality and sexual pleasure.

DISCUSSION

This study will indicate whether progesterone is safe and of additional value with regard to breast volume change in transgender individuals receiving feminizing GAHT. The results of this study will be useful for innovation of feminizing GAHT.

TRIAL REGISTRATION

WHO International Clinical Trials Registry Platform: EUCTR2020-001952-16-NL; date of registration: 12 December 2020 https://trialsearch.who.int/Trial2.aspx?TrialID=EUCTR2020-001952-16-NL .

Frequency of anthropometric implausible values estimated from different methodologies: a systematic review and meta-analysis

CONTEXT

Poor anthropometric data quality affect the prevalence of malnutrition and could harm public policy planning.

OBJECTIVE

This systematic review and meta-analysis was designed to identify different methods to evaluate and clean anthropometric data, and to calculate the frequency of implausible values for weight and height obtained from these methodologies.

DATA SOURCES

Studies about anthropometric data quality and/or anthropometric data cleaning were searched for in the MEDLINE, LILACS, SciELO, Embase, Scopus, Web of Science, and Google Scholar databases in October 2020 and updated in January 2023. In addition, references of included studies were searched for the identification of potentially eligible studies.

DATA EXTRACTION

Paired researchers selected studies, extracted data, and critically appraised the selected publications.

DATA ANALYSIS

Meta-analysis of the frequency of implausible values and 95% confidence interval (CI) was estimated. Heterogeneity (I2) and publication bias were examined by meta-regression and funnel plot, respectively.

RESULTS

In the qualitative synthesis, 123 reports from 104 studies were included, and in the quantitative synthesis, 23 studies of weight and 14 studies of height were included. The study reports were published between 1980 and 2022. The frequency of implausible values for weight was 0.55% (95%CI, 0.29-0.91) and for height was 1.20% (95%CI, 0.44-2.33). Heterogeneity was not affected by the methodological quality score of the studies and publication bias was discarded.

CONCLUSIONS

Height had twice the frequency of implausible values compared with weight. Using a set of indicators of quality to evaluate anthropometric data is better than using indicators singly.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration no. CRD42020208977.

Maternal and child nutrition programme of investigation within the 100 Million Brazilian Cohort: study protocol

INTRODUCTION

There is a limited understanding of the early nutrition and pregnancy determinants of short-term and long-term maternal and child health in ethnically diverse and socioeconomically vulnerable populations within low-income and middle-income countries. This investigation programme aims to: (1) describe maternal weight trajectories throughout the life course; (2) describe child weight, height and body mass index (BMI) trajectories; (3) create and validate models to predict childhood obesity at 5 years of age; (4) estimate the effects of prepregnancy BMI, gestational weight gain (GWG) and maternal weight trajectories on adverse maternal and neonatal outcomes and child growth trajectories; (5) estimate the effects of prepregnancy BMI, GWG, maternal weight and interpregnancy BMI changes on maternal and child outcomes in the subsequent pregnancy; and (6) estimate the effects of maternal food consumption and infant feeding practices on child nutritional status and growth trajectories.

METHODS AND ANALYSIS

Linked data from four different Brazilian databases will be used: the 100 Million Brazilian Cohort, the Live Births Information System, the Mortality Information System and the Food and Nutrition Surveillance System. To analyse trajectories, latent-growth, superimposition by translation and rotation and broken stick models will be used. To create prediction models for childhood obesity, machine learning techniques will be applied. For the association between the selected exposure and outcomes variables, generalised linear models will be considered. Directed acyclic graphs will be constructed to identify potential confounders for each analysis investigating potential causal relationships.

ETHICS AND DISSEMINATION

This protocol was approved by the Research Ethics Committees of the authors' institutions. The linkage will be carried out in a secure environment. After the linkage, the data will be de-identified, and pre-authorised researchers will access the data set via a virtual private network connection. Results will be reported in open-access journals and disseminated to policymakers and the broader public.

k-Means Clustering with Optimal Centroid: An Optimization Insisted Model for Removing Outliers

In data cleaning, the process of detecting and correcting corrupt, inaccurate or irrelevant records from the record set is a tedious task. Particularly, the process of “outlier detection” occupies a significant role in data cleaning that removes or eliminates the outlier’s that exist in data. Traditionally, more efforts have been taken to remove the outliers, and one of the promising ways is customizing clustering models. In this manner, this paper intends to propose a new outlier detection model via enhanced k-means with outlier removal (E-KMOR), which assigns all outliers into a group naturally during the clustering process. For assigning the point to be outliers, a new intra-cluster based distance evaluation is employed. The main contribution of this paper is to select cluster centroid optimally through a newly proposed hybrid optimization algorithm termed particle updated lion algorithm (PU-LA), which hybrids the concepts of LA and particle swarm optimization (PSO), respectively. Thereby, the proposed work is named as E-KMOR-PU-LA. Finally, the efficacy of the proposed E-KMOR-PU-LA model is proved through a comparative analysis over conventional models by concerning runtime and accuracy.

Comparison of cohort characteristics in Central Africa International Epidemiology Databases to Evaluate AIDS and Demographic Health Surveys: Rwanda and Burundi

Clinical health record data are used for HIV surveillance, but the extent to which these data are population representative is not clear. We compared age, marital status, body mass index, and pregnancy distributions in the Central Africa International Databases to Evaluate AIDS (CA-IeDEA) cohorts in Burundi and Rwanda to all people living with HIV and the subpopulation reporting receiving a previous HIV test result in the Demographic and Health Survey (DHS) data, restricted to urban areas, where CA-IeDEA sites are located. DHS uses a probabilistic sample for population-level HIV prevalence estimates. In Rwanda, the CA-IeDEA cohort and DHS populations were similar with respect to age and marital status for men and women, which was also true in Burundi among women. In Burundi, the CA-IeDEA cohort had a greater proportion of younger and single men than the DHS data, which may be a result of outreach to sexual minority populations at CA-IeDEA sites and economic migration patterns. In both countries, the CA-IeDEA cohorts had a higher proportion of underweight individuals, suggesting that symptomatic individuals are more likely to access care in these settings. Multiple sources of data are needed for HIV surveillance to interpret potential biases in epidemiological data.

Multiple burdens of malnutrition and relative remoteness in rural Ecuadorian communities

OBJECTIVE

Social and economic changes associated with new roads can bring about rapid nutritional transitions. To study this process, we: (1) describe trends in adult overweight and obesity (OW/OB) among rural Afro-Ecuadorians over time and across a gradient of community remoteness from the nearest commercial centre; (2) examine the relationship between male and female adult OW/OB and factors associated with market integration such as changing livelihoods and (3) examine the co-occurrence of adult OW/OB and under-five stunting and anaemia.

DESIGN

Adult anthropometry was collected through serial case-control studies repeated over a decade across twenty-eight communities. At the same time, anthropometry and Hb were measured for all children under 5 years of age in every community.

SETTING

Northern coastal Ecuador.

PARTICIPANTS

Adults (n 1665) and children under 5 years of age (n 2618).

RESULTS

From 2003 and 2013, OW/OB increased from 25·1 % to 44·8 % among men and 59·9 % to 70·2 % among women. The inverse relationship between remoteness and OW/OB in men was attenuated when adjusting for urban employment, suggesting that livelihoods mediated the remoteness-OW/OB relationship. No such relationship was observed among women. Communities with a higher prevalence of male OW/OB also had a greater prevalence of stunting, but not anaemia, in children under 5 years of age.

CONCLUSIONS

The association between male OW/OB and child stunting at the community level, but not the household level, suggests that changing food environments, rather than household- or individual-level factors, drove these trends. A closer examination of changing socio-economic structures and food environments in communities undergoing rapid development could help mitigate future public health burdens.

Methods to estimate baseline creatinine and define acute kidney injury in lean Ugandan children with severe malaria: a prospective cohort study

BACKGROUND

Acute kidney injury (AKI) is increasingly recognized as a consequential clinical complication in children with severe malaria. However, approaches to estimate baseline creatinine (bSCr) are not standardized in this unique patient population. Prior to wide-spread utilization, bSCr estimation methods need to be evaluated in many populations, particularly in children from low-income countries.

METHODS

We evaluated six methods to estimate bSCr in Ugandan children aged 6 months to 12 years of age in two cohorts of children with severe malaria (n = 1078) and healthy community children (n = 289). Using isotope dilution mass spectrometry (IDMS)-traceable creatinine measures from community children, we evaluated the bias, accuracy and precision of estimating bSCr using height-dependent and height-independent estimated glomerular filtration (eGFR) equations to back-calculate bSCr or estimating bSCr directly using published or population-specific norms.

RESULTS

We compared methods to estimate bSCr in healthy community children against the IDMS-traceable SCr measure. The Pottel-age based equation, assuming a normal GFR of 120 mL/min per 1.73m², was the more accurate method with minimal bias when compared to the Schwartz height-based equation. Using the different bSCr estimates, we demonstrated the prevalence of KDIGO-defined AKI in children with severe malaria ranged from 15.6-43.4%. The lowest estimate was derived using population upper levels of normal and the highest estimate was derived using the mean GFR of the community children (137 mL/min per 1.73m²) to back-calculate the bSCr. Irrespective of approach, AKI was strongly associated with mortality with a step-wise increase in mortality across AKI stages (p < 0.0001 for all). AKI defined using the Pottel-age based equation to estimate bSCr showed the strongest relationship with mortality with a risk ratio of 5.13 (95% CI 3.03-8.68) adjusting for child age and sex.

CONCLUSIONS

We recommend using height-independent age-based approaches to estimate bSCr in hospitalized children in sub-Saharan Africa due to challenges in accurate height measurements and undernutrition which may impact bSCr estimates. In this population the Pottel-age based GFR estimating equation obtained comparable bSCr estimates to population-based estimates in healthy children.

Artificial Intelligence-Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study

Background

Surgical site infection (SSI) is one of the most common types of health care–associated infections. It increases mortality, prolongs hospital length of stay, and raises health care costs. Many institutions developed risk assessment models for SSI to help surgeons preoperatively identify high-risk patients and guide clinical intervention. However, most of these models had low accuracies.

Objective

We aimed to provide a solution in the form of an Artificial intelligence–based Multimodal Risk Assessment Model for Surgical site infection (AMRAMS) for inpatients undergoing operations, using routinely collected clinical data. We internally and externally validated the discriminations of the models, which combined various machine learning and natural language processing techniques, and compared them with the National Nosocomial Infections Surveillance (NNIS) risk index.

Methods

We retrieved inpatient records between January 1, 2014, and June 30, 2019, from the electronic medical record (EMR) system of Rui Jin Hospital, Luwan Branch, Shanghai, China. We used data from before July 1, 2018, as the development set for internal validation and the remaining data as the test set for external validation. We included patient demographics, preoperative lab results, and free-text preoperative notes as our features. We used word-embedding techniques to encode text information, and we trained the LASSO (least absolute shrinkage and selection operator) model, random forest model, gradient boosting decision tree (GBDT) model, convolutional neural network (CNN) model, and self-attention network model using the combined data. Surgeons manually scored the NNIS risk index values.

Results

For internal bootstrapping validation, CNN yielded the highest mean area under the receiver operating characteristic curve (AUROC) of 0.889 (95% CI 0.886-0.892), and the paired-sample t test revealed statistically significant advantages as compared with other models (P<.001). The self-attention network yielded the second-highest mean AUROC of 0.882 (95% CI 0.878-0.886), but the AUROC was only numerically higher than the AUROC of the third-best model, GBDT with text embeddings (mean AUROC 0.881, 95% CI 0.878-0.884, P=.47). The AUROCs of LASSO, random forest, and GBDT models using text embeddings were statistically higher than the AUROCs of models not using text embeddings (P<.001). For external validation, the self-attention network yielded the highest AUROC of 0.879. CNN was the second-best model (AUROC 0.878), and GBDT with text embeddings was the third-best model (AUROC 0.872). The NNIS risk index scored by surgeons had an AUROC of 0.651.

Conclusions

Our AMRAMS based on EMR data and deep learning methods—CNN and self-attention network—had significant advantages in terms of accuracy compared with other conventional machine learning methods and the NNIS risk index. Moreover, the semantic embeddings of preoperative notes improved the model performance further. Our models could replace the NNIS risk index to provide personalized guidance for the preoperative intervention of SSIs. Through this case, we offered an easy-to-implement solution for building multimodal RAMs for other similar scenarios.

Is it time to stop sweeping data cleaning under the carpet? A novel algorithm for outlier management in growth data

All data are prone to error and require data cleaning prior to analysis. An important example is longitudinal growth data, for which there are no universally agreed standard methods for identifying and removing implausible values and many existing methods have limitations that restrict their usage across different domains. A decision-making algorithm that modified or deleted growth measurements based on a combination of pre-defined cut-offs and logic rules was designed. Five data cleaning methods for growth were tested with and without the addition of the algorithm and applied to five different longitudinal growth datasets: four uncleaned canine weight or height datasets and one pre-cleaned human weight dataset with randomly simulated errors. Prior to the addition of the algorithm, data cleaning based on non-linear mixed effects models was the most effective in all datasets and had on average a minimum of 26.00% higher sensitivity and 0.12% higher specificity than other methods. Data cleaning methods using the algorithm had improved data preservation and were capable of correcting simulated errors according to the gold standard; returning a value to its original state prior to error simulation. The algorithm improved the performance of all data cleaning methods and increased the average sensitivity and specificity of the non-linear mixed effects model method by 7.68% and 0.42% respectively. Using non-linear mixed effects models combined with the algorithm to clean data allows individual growth trajectories to vary from the population by using repeated longitudinal measurements, identifies consecutive errors or those within the first data entry, avoids the requirement for a minimum number of data entries, preserves data where possible by correcting errors rather than deleting them and removes duplications intelligently. This algorithm is broadly applicable to data cleaning anthropometric data in different mammalian species and could be adapted for use in a range of other domains.

Not so implausible: impact of longitudinal assessment of implausible anthropometric measures on obesity prevalence and weight change in children and adolescents

PURPOSE

Implausible anthropometric measures are typically identified using population outlier definitions, conflating implausible and extreme measures. We determined the impact of a longitudinal outlier approach on prevalence of body mass index (BMI) categories and mean change in anthropometric measures in pediatric electronic health record data.

METHODS

We examined 996,131 observations from 147,375 children (10-18 years) in the ADVANCE Clinical Data Research Network, a national network of community health centers. Sex-stratified, mixed effects, linear spline regression modeled weight, height, and BMI as a function of age. Longitudinal outliers were defined as observations with studentized residual greater than |6|; population outliers were defined by Centers for Disease Control-defined z-score thresholds.

RESULTS

At least 99.7% of anthropometric measures were not extreme by longitudinal or population definitions (agreement ≥ 0.995). BMI category prevalence after excluding longitudinal or population outliers differed by less than 0.1%. Among children greater than 85th percentile at baseline, annual mean changes in anthropometric measures were larger in data that excluded longitudinal (girls: 1.24 inches, 12.39 pounds, 1.53 kg/m²; boys: 2.34, 14.08, 1.07) versus population outliers (girls: 0.61 inches, 8.22 pounds, 0.75 kg/m²; boys: 1.53, 11.61, 0.48).

CONCLUSIONS

Longitudinal outlier methods may reduce underestimation of anthropometric change in children with elevated baseline values.

Breast Development in Transwomen After 1 Year of Cross-Sex Hormone Therapy: Results of a Prospective Multicenter Study

CONTEXT

Breast development is a key feature of feminization and therefore important to transwomen (male-to-female transgender persons). It is not exactly known when breast development starts after initiating cross-sex hormone therapy (CHT) and how much growth may be expected.

OBJECTIVE

To investigate breast development in transwomen during their first year of CHT and whether clinical or laboratory parameters predict breast development.

DESIGN

This study was performed as part of the European Network for the Investigation of Gender Incongruence, which is a prospective multicenter cohort study.

SETTING

Gender clinics in Amsterdam, Ghent, and Florence.

PARTICIPANTS

Transwomen who completed the first year of CHT (n = 229).

INTERVENTION

CHT.

MAIN OUTCOME MEASURES

Breast development in centimeter and cup size.

RESULTS

The median age of the included transwomen was 28 years (range, 18 to 69). Mean breast-chest difference increased to 7.9 ± 3.1 cm after 1 year of CHT, mainly resulting in less than an AAA cup size (48.7%). Main breast development occurred in the first 6 months of therapy. Serum estradiol levels did not predict breast development after 1 year of CHT (first quartile, 3.6 cm [95% confidence interval (CI), 2.7 to 4.5], second quartile, 3.2 cm [95% CI, 2.3 to 4.2], third quartile, 4.4 cm [95% CI, 3.5 to 5.3], and fourth quartile, 3.6 cm [95% CI, 2.7 to 4.5]).

CONCLUSION

This study shows that, after 1 year of CHT, breast development is modest and occurs primarily in the first 6 months. No clinical or laboratory parameters were found that predict breast development.

Longitudinal multiple imputation approaches for body mass index or other variables with very low individual-level variability: the mibmi command in Stata

BACKGROUND

In modern health care systems, the computerization of all aspects of clinical care has led to the development of large data repositories. For example, in the UK, large primary care databases hold millions of electronic medical records, with detailed information on diagnoses, treatments, outcomes and consultations. Careful analyses of these observational datasets of routinely collected data can complement evidence from clinical trials or even answer research questions that cannot been addressed in an experimental setting. However, 'missingness' is a common problem for routinely collected data, especially for biological parameters over time. Absence of complete data for the whole of a individual's study period is a potential bias risk and standard complete-case approaches may lead to biased estimates. However, the structure of the data values makes standard cross-sectional multiple-imputation approaches unsuitable. In this paper we propose and evaluate mibmi, a new command for cleaning and imputing longitudinal body mass index data.

RESULTS

The regression-based data cleaning aspects of the algorithm can be useful when researchers analyze messy longitudinal data. Although the multiple imputation algorithm is computationally expensive, it performed similarly or even better to existing alternatives, when interpolating observations.

CONCLUSION

The mibmi algorithm can be a useful tool for analyzing longitudinal body mass index data, or other longitudinal data with very low individual-level variability.

[The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement]

Zunehmend werden routinemäßig gesammelte Gesundheitsdaten, die zu administrativen und klinischen Zwecken und ohne spezifische, a priori festgelegte Forschungsziele erhoben wurden, auch für die Forschung eingesetzt. Die rasche Entwicklung und Verfügbarkeit dieser Daten machten Probleme deutlich, die in den bestehenden Berichts-Leitlinien, wie dem STROBE-Statement (Strengthening the Reporting of Observational Studies in Epidemiology) nicht behandelt werden. Das RECORD-Statement (REporting of studies Conducted using Observational Routinely-collected health Data) wurde entwickelt, um diese Lücken zu schließen. RECORD ist als Erweiterung des STROBE-Statements gedacht, um Punkte abzudecken, die spezifisch sind beim Berichten von Beobachtungsstudien, die routinemäßig gesammelte Gesundheitsdaten verwenden. RECORD besteht aus einer Checkliste von 13 Punkten mit Bezug zu Titel, Abstract, Einleitung, Methoden-, Ergebnis- und Diskussionsteil von Artikeln sowie zu anderen Informationen, die in Forschungsberichten dieser Art enthalten sein sollten. Dieses Dokument enthält die Checkliste sowie Erläuterungen und weitere Erklärungen, um die Verwendung der Checkliste zu verbessern. Beispiele für ein gutes Berichten der einzelnen Punkte der RECORD-Checkliste sind ebenfalls in diesem Dokument enthalten. Dieses Dokument sowie die zugehörige Website und ein Forum (http://www.record-statement.org) werden die Umsetzung und das Verständnis von RECORD verbessern. Autoren, Redakteure von Fachzeitschriften und Peer-Reviewer können die Transparenz beim Berichten von Forschungsergebnissen erhöhen, indem sie RECORD anwenden.

The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement

Routinely collected health data, obtained for administrative and clinical purposes without specific a priori research goals, are increasingly used for research. The rapid evolution and availability of these data have revealed issues not addressed by existing reporting guidelines, such as Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). The REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement was created to fill these gaps. RECORD was created as an extension to the STROBE statement to address reporting items specific to observational studies using routinely collected health data. RECORD consists of a checklist of 13 items related to the title, abstract, introduction, methods, results, and discussion section of articles, and other information required for inclusion in such research reports. This document contains the checklist and explanatory and elaboration information to enhance the use of the checklist. Examples of good reporting for each RECORD checklist item are also included herein. This document, as well as the accompanying website and message board (http://www.record-statement.org), will enhance the implementation and understanding of RECORD. Through implementation of RECORD, authors, journals editors, and peer reviewers can encourage transparency of research reporting.

The value of aspartate aminotransferase and alanine aminotransferase in cardiovascular disease risk assessment

OBJECTIVE

Aspartate aminotransferase to alanine aminotransferase (AST/ALT) ratio, reflecting liver disease severity, has been associated with increased risk of cardiovascular disease (CVD). The aim of this study was to evaluate whether the AST/ALT ratio improves established risk prediction tools in a primary care population.

METHODS

Data were analysed from a prospective cohort of 29 316 UK primary care patients, aged 25-84 years with no history of CVD at baseline. Cox proportional hazards regression was used to derive 10-year multivariate risk models for the first occurrence of CVD based on two established risk prediction tools (Framingham and QRISK2), with and without including the AST/ALT ratio. Overall, model performance was assessed by discriminatory accuracy (AUC c-statistic).

RESULTS

During a total follow-up of 120 462 person-years, 782 patients (59% men) experienced their first CVD event. Multivariate models showed that elevated AST/ALT ratios were significantly associated with CVD in men (Framingham: HR 1.37, 95% CI 1.05 to 1.79; QRISK2: HR 1.40, 95% CI 1.04 to 1.89) but not in women (Framingham: HR 1.06, 95% CI 0.78 to 1.43; QRISK2: HR 0.97, 95% CI 0.70 to 1.35). Including the AST/ALT ratio with all Framingham risk factors (AUC c-statistic: 0.72, 95% CI 0.71 to 0.74) or QRISK2 risk factors (AUC c-statistic: 0.73, 95% CI 0.71 to 0.74) resulted in no change in discrimination from the established risk prediction tools. Limiting analysis to those individuals with raised ALT showed that discrimination could improve by 5% and 4% with Framingham and QRISK2 risk factors, respectively.

CONCLUSIONS

Elevated AST/ALT ratio is significantly associated with increased risk of developing CVD in men but not women. However, the ratio does not confer any additional benefits over established CVD risk prediction tools in the general population, but may have clinical utility in certain subgroups.

Detection of Outliers Due to Participants' Non-Adherence to Protocol in a Longitudinal Study of Cognitive Decline

Background

Participants’ non adherence to protocol affects data quality. In longitudinal studies, this leads to outliers that can be present at the level of the population or the individual. The purpose of the present study is to elaborate a method for detection of outliers in a study of cognitive ageing.

Methods

In the Whitehall II study, data on a cognitive test battery have been collected in 1997-99, 2002-04, 2007-09 and 2012-13. Outliers at the 2012-13 wave were identified using a 4-step procedure: (1) identify cognitive tests with potential non-adherence to protocol, (2) choose a prediction model between a simple model with socio-demographic covariates and one that also includes health behaviours and health measures, (3) define an outlier using a studentized residual, and (4) study the impact of exclusion of outliers by estimating the effect of age and diabetes on cognitive decline.

Results

5516 participants provided cognitive data in 2012-13. Comparisons of rates of annual decline over the first three and all four waves of data suggested outliers in three of the 5 tests. Mean residuals for the 2012-13 wave were larger for the basic compared to the more complex prediction model (all p<0.001), leading us to use the latter for the identification of outliers. Residuals greater than two standard deviation of residuals identified approximately 7% of observations as being outliers. Removal of these observations from the analyses showed that both age and diabetes had associations with cognitive decline similar to that observed with the first three waves of data; these associations were weaker or absent in non-cleaned data.

Conclusions

Identification of outliers is important as they obscure the effects of known risk factor and introduce bias in the estimates of cognitive decline. We showed that an informed approach, using the range of data collected in a longitudinal study, may be able to identify outliers.