Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study

Demographic, health, physical activity, and workplace factors are associated with lower healthy working life expectancy and life expectancy at age 50

Although retirement ages are rising in the United Kingdom and other countries, the average number of years people in England can expect to spend both healthy and work from age 50 (Healthy Working Life Expectancy; HWLE) is less than the number of years to the State Pension age. This study aimed to estimate HWLE with the presence and absence of selected health, socio-demographic, physical activity, and workplace factors relevant to stakeholders focusing on improving work participation. Data from 11,540 adults in the English Longitudinal Study of Ageing were analysed using a continuous time 3-state multi-state model. Age-adjusted hazard rate ratios (aHRR) were estimated for transitions between health and work states associated with individual and combinations of health, socio-demographic, and workplace factors. HWLE from age 50 was 3.3 years fewer on average for people with pain interference (6.54 years with 95% confidence interval [6.07, 7.01]) compared to those without (9.79 [9.50, 10.08]). Osteoarthritis and mental health problems were associated with 2.2 and 2.9 fewer healthy working years respectively (HWLE for people without osteoarthritis: 9.50 years [9.22, 9.79]; HWLE with osteoarthritis: 7.29 years [6.20, 8.39]; HWLE without mental health problems: 9.76 years [9.48, 10.05]; HWLE with mental health problems: 6.87 years [1.58, 12.15]). Obesity and physical inactivity were associated with 0.9 and 2.0 fewer healthy working years respectively (HWLE without obesity: 9.31 years [9.01, 9.62]; HWLE with obesity: 8.44 years [8.02, 8.86]; HWLE without physical inactivity: 9.62 years [9.32, 9.91]; HWLE with physical inactivity: 7.67 years [7.23, 8.12]). Workers without autonomy at work or with inadequate support at work were expected to lose 1.8 and 1.7 years respectively in work with good health from age 50 (HWLE for workers with autonomy: 9.50 years [9.20, 9.79]; HWLE for workers lacking autonomy: 7.67 years [7.22, 8.12]; HWLE for workers with support: 9.52 years [9.22, 9.82]; HWLE for workers with inadequate support: 7.86 years [7.22, 8.12]). This study identified demographic, health, physical activity, and workplace factors associated with lower HWLE and life expectancy at age 50. Identifying the extent of the impact on healthy working life highlights these factors as targets and the potential to mitigate against premature work exit is encouraging to policy-makers seeking to extend working life as well as people with musculoskeletal and mental health conditions and their employers. The HWLE gaps suggest that interventions are needed to promote the health, wellbeing and work outcomes of subpopulations with long-term health conditions.

Early inflammatory markers as prognostic indicators following allogeneic stem cell transplantation

Allogeneic stem cell transplantation is used widely in the treatment of hematopoietic malignancy although graft versus host disease and relapse remain major complications. We measured the serum protein expression of 92 inflammation-related markers from 49 patients at Day 0 (D0) and 154 patients at Day 14 (D14) following transplantation and related values to subsequent clinical outcomes. Low levels of 7 proteins at D0 were linked to GvHD whilst high levels of 7 proteins were associated with relapse. The concentration of 38 proteins increased over 14 days and higher inflammatory response at D14 was strongly correlated with patient age. A marked increment in protein concentration during this period associated with GvHD but reduced risk of disease relapse, indicating a link with alloreactive immunity. In contrast, patients who demonstrated low dynamic elevation of inflammatory markers during the first 14 days were at increased risk of subsequent disease relapse. Multivariate time-to-event analysis revealed that high CCL23 at D14 was associative of AGvHD, CXCL10 with reduced rate of relapse, and high PD-L1 with reduced overall survival. This work identifies a dynamic pattern of inflammatory biomarkers in the very early post-transplantation period and reveals early protein markers that may help to guide patient management.

The Influence of Airborne Particulate Matter on the Risk of Gestational Diabetes Mellitus: A Large Retrospective Study in Chongqing, China

Emerging research findings suggest that airborne particulate matter might be a risk factor for gestational diabetes mellitus (GDM). However, the concentration-response relationships and the susceptible time windows for different types of particulate matter may vary. In this retrospective analysis, we employ a novel robust approach to assess the crucial time windows regarding the prevalence of GDM and to distinguish the susceptibility of three GDM subtypes to air pollution exposure. This study included 16,303 pregnant women who received routine antenatal care in 2018-2021 at the Maternal and Child Health Hospital in Chongqing, China. In total, 2482 women (15.2%) were diagnosed with GDM. We assessed the individual daily average exposure to air pollution, including PM2.5, PM10, O3, NO2, SO2, and CO based on the volunteers' addresses. We used high-accuracy gridded air pollution data generated by machine learning models to assess particulate matter per maternal exposure levels. We further analyzed the association of pre-pregnancy, early, and mid-pregnancy exposure to environmental pollutants using a generalized additive model (GAM) and distributed lag nonlinear models (DLNMs) to analyze the association between exposure at specific gestational weeks and the risk of GDM. We observed that, during the first trimester, per IQR increases for PM10 and PM2.5 exposure were associated with increased GDM risk (PM10: OR = 1.19, 95%CI: 1.07~1.33; PM2.5: OR = 1.32, 95%CI: 1.15~1.50) and isolated post-load hyperglycemia (GDM-IPH) risk (PM10: OR = 1.23, 95%CI: 1.09~1.39; PM2.5: OR = 1.38, 95%CI: 1.18~1.61). Second-trimester O3 exposure was positively correlated with the associated risk of GDM, while pre-pregnancy and first-trimester exposure was negatively associated with the risk of GDM-IPH. Exposure to SO2 in the second trimester was negatively associated with the risk of GDM-IPH. However, there were no observed associations between NO2 and CO exposure and the risk of GDM and its subgroups. Our results suggest that maternal exposure to particulate matter during early pregnancy and exposure to O3 in the second trimester might increase the risk of GDM, and GDM-IPH is the susceptible GDM subtype to airborne particulate matter exposure.

A longitudinal transition imputation model for categorical data applied to a large registry dataset

Imputation of longitudinal categorical covariates with several waves and many predictors is cumbersome in terms of implausible transitions, colinearity, and overfitting. We designed a simulation study with data obtained from a general practitioners' morbidity registry in Belgium for three waves, with smoking as the longitudinal covariate of interest. We set varying proportions of data on smoking to missing completely at random and missing not at random with proportions of missingness equal to 10%, 30%, 50%, and 70%. This study proposed a 3-stage approach that allows flexibility when imputing time-dependent categorical covariates. First, multiple imputation using fully conditional specification or multiple imputation for the predictor variables was deployed using the wide format such that previous and future information of the same patient was utilized. Second, a joint Markov transition model for initial, forward, backward, and intermittent probabilities was developed for each imputed dataset. Finally, this transition model was used for imputation. We compared the performance of this methodology with an analyses of the complete data and with listwise deletion in terms of bias and root mean square error. Next, we applied this methodology in a clinical case for years 2017 to 2021, where we estimated the effect of several covariates on the pneumococcal vaccination. This methodological framework ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. Finally, a companion R package was developed to enable the replication and easy application of this methodology.

Mean versus variability of lipid measurements over 6 years and incident cardiovascular events: More than a decade follow-up

Background

Lipid variability (LV) has emerged as a contributor to the incidence of cardiovascular diseases (CVD), even after considering the effect of mean lipid levels. However, these associations have not been examined among people in the Middle East and North Africa (MENA) region. We aimed to investigate the association of 6-year mean lipid levels versus lipid variability with the risk of CVD among an Iranian population.

Methods

A total of 3,700 Iranian adults aged ≥ 30 years, with 3 lipid profile measurements, were followed up for incident CVD until March 2018. Lipid variability was measured as standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and variability independent of mean (VIM). The effects of mean lipid levels and LV on CVD risk were assessed using multivariate Cox proportional hazard models.

Results

During a median 14.5-year follow-up, 349 cases of CVD were recorded. Each 1-SD increase in the mean levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), TC/high-density lipoprotein cholesterol (HDL-C), and non-HDL-C increased the risk of CVD by about 26-29%; for HDL-C, the risk was significantly lower by 12% (all p-values < 0.05); these associations resisted after adjustment for their different LV indices. Considering LV, each 1-SD increment in SD and ARV variability indices for TC and TC/HDL-C increased the risk of CVD by about 10%; however, these associations reached null after further adjustment for their mean values. The effect of TC/HDL-C variability (measured as SD) and mean lipid levels, except for LDL-C, on CVD risk was generally more pronounced in the non-elderly population.

Conclusion

Six-year mean lipid levels were associated with an increased future risk of incident CVD, whereas LV were not. Our findings highlight the importance of achieving normal lipid levels over time, but not necessarily consistent, for averting adverse clinical outcomes.

Month 60 Outcomes After Treatment Initiation With Anti-Vascular Endothelial Growth Factor Therapy for Macular Edema Due to Central Retinal or Hemiretinal Vein Occlusion

PURPOSE

To investigate 5-year outcomes in eyes initially treated with aflibercept or bevacizumab for macular edema due to central retinal or hemiretinal vein occlusion.

METHODS

Long-term follow-up (LTF) after a randomized clinical trial from 64 centers in the United States. Participants were followed up to 60 months and treated at investigator discretion after completing the 12-month treatment protocol. Main outcomes were visual acuity letter score (VALS) and central subfield thickness (CST) on optical coherence tomography.

RESULTS

Seventy-five percent (248/330) of eligible participants completed at least 1 visit between months 24 and 60, and 45% completed the month 60 visit. Among participants completing month 60, overall mean VALS improvement over baseline was 13.5 (95% CI: 9.6, 17.5), less than the mean improvement of 20.6 (95% CI: 18.7, 22.4) observed at month 12, with no significant differences between originally assigned study groups. Further, 66% (99/150) had at least 1 treatment between months 48 and 60 with a mean (SD) of 3.41 (3.69) treatments over this period. Mean CST was 671 μm at baseline and 261 μm (95% CI: 241.2, 280.9) at month 60.

CONCLUSIONS

Although VALS improved substantially when patients were treated per protocol through month 12, improvement lessened when treatment was at investigator discretion and fewer treatments were received although VALS remained markedly improved over baseline through year 5. Most patients continued to receive treatment in year 5. This suggests that continued monitoring and, if warranted, treatment with anti-VEGF therapy benefits patients with macular edema associated with central retinal or hemiretinal vein occlusion. Publication of this article is sponsored by the American Ophthalmological Society.

Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential

Clinical outcomes for patients with COVID-19 are heterogeneous and there is interest in defining subgroups for prognostic modeling and development of treatment algorithms. We obtained 28 demographic and laboratory variables in patients admitted to hospital with COVID-19. These comprised a training cohort (n = 6099) and two validation cohorts during the first and second waves of the pandemic (n = 996; n = 1011). Uniform manifold approximation and projection (UMAP) dimension reduction and Gaussian mixture model (GMM) analysis was used to define patient clusters. 29 clusters were defined in the training cohort and associated with markedly different mortality rates, which were predictive within confirmation datasets. Deconvolution of clinical features within clusters identified unexpected relationships between variables. Integration of large datasets using UMAP-assisted clustering can therefore identify patient subgroups with prognostic information and uncovers unexpected interactions between clinical variables. This application of machine learning represents a powerful approach for delineating disease pathogenesis and potential therapeutic interventions.

Potential benefits, unintended consequences, and future roles of artificial intelligence in orthopaedic surgery research : a call to emphasize data quality and indications

Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.

Automated machine learning-based model predicts postoperative delirium using readily extractable perioperative collected electronic data

Objective

Postoperative delirium (POD) is a common postoperative complication that is relevant to poor outcomes. Therefore, it is critical to find effective methods to identify patients with high risk of POD rapidly. Creating a fully automated score based on an automated machine‐learning algorithm may be a method to predict the incidence of POD quickly.

Materials and methods

This is the secondary analysis of an observational study, including 531 surgical patients who underwent general anesthesia. The least absolute shrinkage and selection operator (LASSO) was used to screen essential features associated with POD. Finally, eight features (age, intraoperative blood loss, anesthesia duration, extubation time, intensive care unit [ICU] admission, mini‐mental state examination score [MMSE], Charlson comorbidity index [CCI], postoperative neutrophil‐to‐lymphocyte ratio [NLR]) were used to established models. Four models, logistic regression, random forest, extreme gradient boosted trees, and support vector machines, were built in a training set (70% of participants) and evaluated in the remaining testing sample (30% of participants). Multivariate logistic regression analysis was used to explore independent risk factors for POD further.

Results

Model 1 (logistic regression model) was found to outperform other classifier models in testing data (area under the curve [AUC] of 80.44%, 95% confidence interval [CI] 72.24%–88.64%) and achieve the lowest Brier Score as well. These variables including age (OR = 1.054, 95%CI: 1.017~1.093), extubation time (OR = 1.027, 95%CI: 1.012~1.044), ICU admission (OR = 2.238, 95%CI: 1.313~3.793), MMSE (OR = 0.929, 95%CI: 0.876~0.984), CCI (OR = 1.197, 95%CI: 1.038~1.384), and postoperative NLR (OR = 1.029, 95%CI: 1.002~1.057) were independent risk factors for POD in this study.

Conclusions

We have built and validated a high‐performing algorithm to demonstrate the extent to which patient risk changes of POD during the perioperative period, thus leading to a rational therapeutic choice.

Application of Machine Learning Algorithms to Predict Clinically Meaningful Improvement After Arthroscopic Anterior Cruciate Ligament Reconstruction

Background:

Understanding specific risk profiles for each patient and their propensity to experience clinically meaningful improvement after anterior cruciate ligament reconstruction (ACLR) is important for preoperative patient counseling and management of expectations.

Purpose:

To develop machine learning algorithms to predict achievement of the minimal clinically important difference (MCID) on the International Knee Documentation Committee (IKDC) score at a minimum 2-year follow-up after ACLR.

Study Design:

Case-control study; Level of evidence, 3.

Methods:

An ACLR registry of patients from 27 fellowship-trained sports medicine surgeons at a large academic institution was retrospectively analyzed. Thirty-six variables were tested for predictive value. The study population was randomly partitioned into training and independent testing sets using a 70:30 split. Six machine learning algorithms (stochastic gradient boosting, random forest, neural network, support vector machine, adaptive gradient boosting, and elastic-net penalized logistic regression [ENPLR]) were trained using 10-fold cross-validation 3 times and internally validated on the independent set of patients. Algorithm performance was assessed using discrimination, calibration, Brier score, and decision-curve analysis.

Results:

A total of 442 patients, of whom 39 (8.8%) did not achieve the MCID, were included. The 5 most predictive features of achieving the MCID were body mass index ≤27.4, grade 0 medial collateral ligament examination (compared with other grades), intratunnel femoral tunnel fixation (compared with suspensory), no history of previous contralateral knee surgery, and achieving full knee extension preoperatively. The ENPLR algorithm had the best relative performance (C-statistic, 0.82; calibration intercept, 0.10; calibration slope, 1.15; Brier score, 0.068), demonstrating excellent predictive ability in the study’s data set.

Conclusion:

Machine learning, specifically the ENPLR algorithm, demonstrated good performance for predicting a patient’s propensity to achieve the MCID for the IKDC score after ACLR based on preoperative and intraoperative factors. The femoral tunnel fixation method was the only significant intraoperative variable. Range of motion and medial collateral ligament integrity were found to be important physical examination parameters. Increased body mass index and prior contralateral surgery were also significantly predictive of outcome.

Effect of nocturnal oxygen therapy on exercise performance of COPD patients at 2048 m: data from a randomized clinical trial

This trial evaluates whether nocturnal oxygen therapy (NOT) during a stay at 2048 m improves altitude-induced exercise intolerance in lowlanders with chronic obstructive pulmonary disease (COPD). 32 lowlanders with moderate to severe COPD, mean ± SD forced expiratory volume in the first second of expiration (FEV₁) 54 ± 13% predicted, stayed for 2 days at 2048 m twice, once with NOT, once with placebo according to a randomized, crossover trial with a 2-week washout period at < 800 m in-between. Semi-supine, constant-load cycle exercise to exhaustion at 60% of maximal work-rate was performed at 490 m and after the first night at 2048 m. Endurance time was the primary outcome. Additional outcomes were cerebral tissue oxygenation (CTO), arterial blood gases and breath-by-breath measurements (http://www.ClinicalTrials.gov NCT02150590). Mean ± SE endurance time at 490 m was 602 ± 65 s, at 2048 m after placebo 345 ± 62 s and at 2048 m after NOT 293 ± 60 s, respectively (P < 0.001 vs. 490 m). Mean difference (95%CI) NOT versus placebo was − 52 s (− 174 to 70), P = 0.401. End-exercise pulse oximetry (SpO₂), CTO and minute ventilation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{\text{V}}}_{{\text{E}}}$$\end{document}V˙E) at 490 m were: SpO₂ 92 ± 1%, CTO 65 ± 1%, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{\text{V}}}_{{\text{E}}}$$\end{document}V˙E 37.7 ± 2.0 L/min; at 2048 m with placebo: SpO₂ 85 ± 1%, CTO 61 ± 1%, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{\text{V}}}_{{\text{E}}}$$\end{document}V˙E 40.6 ± 2.0 L/min and with NOT: SpO₂ 84 ± 1%; CTO 61 ± 1%; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{\text{V}}}_{{\text{E}}}$$\end{document}V˙E 40.6 ± 2.0 L/min (P < 0.05, SpO₂, CTO at 2048 m with placebo vs. 490 m; P = NS, NOT vs. placebo). Altitude-related hypoxemia and cerebral hypoxia impaired exercise endurance in patients with moderate to severe COPD and were not prevented by NOT.

Assessment of early COVID-19 compliance to and challenges with public health and social prevention measures in the Kingdom of Eswatini, using an online survey

Public health and social measures have been implemented around the world in a bid to prevent the spread of COVID-19. Public compliance with these measures is key in successfully controlling the pandemic. This online survey assessed the compliance and attitude of adults residing in the southern African Kingdom of Eswatini to government protection, activity and travel measures aimed at controlling the spread of COVID-19. A rapid online survey, comprising of 28 questions, was administered in May 2020. More than 90% of respondents knew the virus could kill anyone and most respondents (70%) reported to be compliant to public health and social measures. Females, those who did not use public transport and those aged 30 years and above were significantly (p<0.01) more compliant, particularly to protective and travel measures. Social media, television and official government websites were the primary source of ongoing COVID-19 information for respondents of this online survey, and these methods should continue to be employed to reach the public who regularly use the internet. More than half of essential workers who responded to the online survey reported to have their own personal protective equipment; however, 32% either did not have any protective equipment or shared their equipment with other staff members. Due to the survey being online, these results should not be generalised to populations of low socioeconomic status.

Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting Clinically Significant Functional Improvement in a Mixed Population of Primary Hip Arthroscopy

PURPOSE

To (1) develop and validate a machine learning algorithm to predict clinically significant functional improvements after hip arthroscopy for femoroacetabular impingement syndrome and to (2) develop a digital application capable of providing patients with individual risk profiles to determine their propensity to gain clinically significant improvements in function.

METHODS

A retrospective review of consecutive hip arthroscopy patients who underwent cam/pincer correction, labral preservation, and capsular closure between January 2012 and 2017 from 1 large academic and 3 community hospitals operated on by a single high-volume hip arthroscopist was performed. The primary outcome was the minimal clinically important difference (MCID) for the Hip Outcome Score (HOS)-Activities of Daily Living (ADL) at 2 years postoperatively, which was calculated using a distribution-based method. A total of 21 demographic, radiographic, and patient-reported outcome measures were considered as potential covariates. An 80:20 random split was used to create training and testing sets from the patient cohort. Five supervised machine learning algorithms were developed using 3 iterations of 10-fold cross-validation on the training set and assessed by discrimination, calibration, Brier score, and decision curve analysis on an independent testing set of patients.

RESULTS

A total of 818 patients with a median (interquartile range) age of 32.0 (22.0-42.0) and 69.2% female were included, of whom 74.3% achieved the MCID for the HOS-ADL. The best-performing algorithm was the stochastic gradient boosting model (c-statistic = 0.84, calibration intercept = 0.20, calibration slope = 0.83, and Brier score = 0.13). Of the initial 21 candidate variables, the 8 most important features for predicting the MCID for the HOS-ADL included in model training were body mass index, age, preoperative HOS-ADL score, preoperative pain level, sex, Tönnis grade, symptom duration, and drug allergies. The algorithm was subsequently transformed into a digital application using local explanations to provide customized risk assessment: https://orthoapps.shinyapps.io/HPRG_ADL/.

CONCLUSIONS

The stochastic boosting gradient model conferred excellent predictive ability for propensity to gain clinically significant improvements in function after hip arthroscopy. An open-access digital application was created, which may augment shared decision-making and allow for preoperative risk stratification. External validation of this model is warranted to confirm the performance of these algorithms, as the generalizability is currently unknown.

LEVEL OF EVIDENCE

IV, Case series.

Machine Learning Algorithms Predict Clinically Significant Improvements in Satisfaction After Hip Arthroscopy

PURPOSE

To develop machine learning algorithms to predict failure to achieve clinically significant satisfaction after hip arthroscopy.

METHODS

We queried a clinical repository for consecutive primary hip arthroscopy patients treated between January 2012 and January 2017. Five supervised machine learning algorithms were developed in a training set of patients and internally validated in an independent testing set of patients by discrimination, Brier score, calibration, and decision-curve analysis. The minimal clinically important difference (MCID) for the visual analog scale (VAS) score for satisfaction was derived by an anchor-based method and used as the primary outcome.

RESULTS

A total of 935 patients were included, of whom 148 (15.8%) did not achieve the MCID for the VAS satisfaction score at a minimum of 2 years postoperatively. The best-performing algorithm was the neural network model (C statistic, 0.94; calibration intercept, -0.43; calibration slope, 0.94; and Brier score, 0.050). The 5 most important features to predict failure to achieve the MCID for the VAS satisfaction score were history of anxiety or depression, lateral center-edge angle, preoperative symptom duration exceeding 2 years, presence of 1 or more drug allergies, and Workers' Compensation.

CONCLUSIONS

Supervised machine learning algorithms conferred excellent discrimination and performance for predicting clinically significant satisfaction after hip arthroscopy, although this analysis was performed in a single population of patients. External validation is required to confirm the performance of these algorithms.

LEVEL OF EVIDENCE

Level III, therapeutic case-control study.

The dynamics of metabolic syndrome development from its isolated components among Iranian adults: findings from 17 years of the Tehran lipid and glucose study (TLGS)

Background

Evaluating the process of changes in the Metabolic Syndrome (MetS) components over time is one of the ways to study of the MetS natural history. This study aimed to determine the trend of changes in the progression of MetS from its isolated components.

Methods

This longitudinal study was performed on four follow-up periods of the Tehran Lipid and Glucose Study (TLGS) between 1999 and 2015. The research population consisted of 3905 adults over the age of 18 years. MetS was diagnosed based on the Joint Interim Statement (JIS). The considered components were abdominal obesity, hypertension, hyperglycemia, and dyslipidemia.

Results

The highest incidence of MetS from its components was related to hypertension in the short term (3.6-year intervals). In the long run, however, the highest increase in the MetS incidence occurred due to abdominal obesity. Overall, the incidence of MetS increased due to obesity and dyslipidemia, but decreased due to the other factors. Nonetheless, the trend of MetS incidence from all components increased in total. The most common components were dyslipidemia with a decreasing trend and obesity with an increasing trend during the study.

Conclusion

The results indicated that obesity and hypertension components played a more important role in the further development of MetS compared to other components in the Iranian adult population. This necessitates careful and serious attention in preventive and control planning.

The dynamics of metabolic syndrome development from its isolated components among iranian children and adolescents: Findings from 17 Years of the Tehran Lipid and Glucose Study (TLGS)

BACKGROUND

Careful evaluation of the progression trend of the metabolic syndrome (MetS) in children and adolescents (C&A) is one of the important methods of studying the natural history of MetS in them. This study was performed to determine the trend of changes in the progression of MetS from its components.

METHODS

This was a longitudinal study which was performed on data from 4 follow-up periods of Tehran Lipid and Glucose Study (TLGS) between 1999 and 2015. The research population consisted of 6-18-year-old children and adolescents creating 3895-person population. The criteria for the diagnosis of MetS was joint interim statement (JIS). The considered components were central adiposity, high blood pressure, insulin resistance, and dyslipidemia.

RESULTS

In this study, in the long term, the highest increase in the MetS' incidence in boys occurred in obesity and in girls in dyslipidemia and in total mode, in obesity. But in the short term (3.6 year follow-up periods) in the first to fourth periods, in total mode, the highest incidence occurred in dyslipidemia, hyperglycemia, dyslipidemia, and obesity. In terms of trend, in total mode, the highest increase in MetS incidence was related to the obesity component. Also, the incidence of MetS from all components was declining in overall mode. Also, the most common components at the beginning and end of the study in all groups were dyslipidemia with a decreasing and obesity with an increasing trend, respectively.

CONCLUSION

It seems that in Iranian C&As, obesity and dyslipidemia components play a more important role in the further development of the MetS than other components. This matter requires careful and serious attention in preventive and control planning.

Development of Machine Learning Algorithms to Predict Patient Dissatisfaction After Primary Total Knee Arthroplasty

BACKGROUND

Postoperative dissatisfaction after primary total knee arthroplasty (TKA) that requires additional care or readmission may impose a significant financial burden to healthcare systems. The purpose of the current study is to develop machine learning algorithms to predict dissatisfaction after TKA.

METHODS

A retrospective review of consecutive TKA patients between 2014 and 2016 from 1 large academic and 2 community hospitals was performed. Preoperative variables considered for prediction included demographics, medical history, flexion contracture, knee flexion, and outcome scores (patient-reported health state, Knee Society Score [KSS], and KSS-Function [KSS-F]). Recursive feature elimination was used to select features that optimized algorithm performance. Five supervised machine learning algorithms were developed by training with 10-fold cross-validation 3 times. These algorithms were subsequently applied to an independent testing set of patients and assessed by discrimination, calibration, Brier score, and decision curve analysis.

RESULTS

Of 430 patients, a total of 40 (9.0%) were dissatisfied with their outcome after primary TKA at a minimum of 2 years postoperatively. The random forest algorithm achieved the best performance in the independent testing set not used for algorithm development (c-statistic: 0.77, calibration intercept: 0.087, calibration slope: 0.74, Brier score: 0.082). The most important factors for predicting dissatisfaction were age, number of medical comorbidities, presence of one or more drug allergies, preoperative patient-reported health state score, and preoperative KSS.

CONCLUSION

The current study developed machine learning algorithms based on partially modifiable risk factors for predicting dissatisfaction after TKA. This model demonstrates good discriminative capacity for identifying those at greatest risk for dissatisfaction and may allow for preoperative health optimization.

Multiple imputation methods for handling missing values in longitudinal studies with sampling weights: Comparison of methods implemented in Stata

Many analyses of longitudinal cohorts require incorporating sampling weights to account for unequal sampling probabilities of participants, as well as the use of multiple imputation (MI) for dealing with missing data. However, there is no guidance on how MI and sampling weights should be implemented together. We simulated a target population based on the Australian Bureau of Statistics Estimated Resident Population and drew 1000 random samples dependent on three design variables to mimic the Longitudinal Study of Australian Children. The target analysis was the weighted prevalence of overweight/obesity over childhood. We evaluated the performance of several MI approaches available in Stata, based on multivariate normal imputation (MVNI), fully conditional specification (FCS) and twofold FCS: a weighted imputation model, imputing missing data separately for each quintile sampling weight grouping, including the design stratum indicator in the imputation model, and using sampling weights as a covariate in the imputation model. Approaches based on available cases and inverse probability weighting (IPW), with time-varying weights, were also compared. We observed severe issues of convergence with FCS and twofold FCS. All MVNI-based approaches performed similarly, producing minimal bias and nominal coverage, except for when imputation was conducted separately for each quintile sampling weight group. IPW performed equally as well as MVNI-based approaches in terms of bias, however, was less precise. In similar longitudinal studies, we recommend using MVNI with the design stratum as a covariate in the imputation model. If this is unknown, including the sampling weight as a covariate is an appropriate alternative.

Assessing a WeChat-Based Integrative Family Intervention (WIFI) for Schizophrenia: Protocol for a Stepped-Wedge Cluster Randomized Trial

BACKGROUND

Schizophrenia is a persistent and debilitating mental illness, and its prognosis depends largely on supportive care and systematic treatment. In developing countries like China, families constitute the major caregiving force for schizophrenia and are faced with many challenges, such as lack of knowledge, skills, and resources. The approach to support family caregiving in an accessible, affordable, feasible, and cost-effective way remains unclear. The wide-spread use of WeChat provides a promising and cost-effective medium for support.

OBJECTIVE

We aim to present a protocol for assessing a WeChat-based integrative family intervention (WIFI) to support family caregiving for schizophrenia.

METHODS

We will develop a WIFI program that includes the following three core components: (1) psychoeducation (WeChat official account), (2) peer support (WeChat chat group), and (3) professional support (WeChat video chat). A rigorous stepped-wedge cluster randomized trial will be used to evaluate the implementation, effectiveness, and cost of the WIFI program. The WIFI program will be implemented in 12 communities affiliated with Changsha Psychiatric Hospital through the free medicine delivery process in the 686 Program. The 12 communities will be randomized to one of four fixed sequences every 2 months during an 8-month intervention period in four clusters of three communities each. Outcomes will be assessed for both family caregivers and people with schizophrenia. Family caregivers will be assessed for their knowledge and skills about caregiving, social support, coping, perceived stigma, caregiver burden, family functioning, positive feelings, and psychological distress. People with schizophrenia will be assessed for their symptoms, functioning, quality of life, recovery, and rehospitalization. Cost data, such as intervention costs, health care utilization costs, and costs associated with lost productivity, will be collected. Moreover, we will collect process data, including fidelity and quality of program implementation, as well as user attitude data. Treatment effects will be estimated using generalized linear maximum likelihood mixed modeling with clusters as a random effect and time as a fixed effect. Cost-effectiveness analysis will be performed from the societal perspective using incremental cost-effectiveness ratios. Qualitative analysis will use the grounded theory approach and immersion-crystallization process.

RESULTS

The study was funded in August 2018 and approved by the institutional review board on January 15, 2019. Preliminary baseline data collection was conducted in May 2019 and completed in September 2019. The WIFI program is expected to start in September 2020.

CONCLUSIONS

This is the first study to assess a WeChat-based mHealth intervention to support family caregiving for schizophrenia in China. The innovative study will contribute to the development of a more cost-effective and evidence-based family management model in the community for people with schizophrenia, and the approach could potentially be integrated into national policy and adapted for use in other populations.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04393896; https://clinicaltrials.gov/ct2/show/NCT04393896.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/18538.