Inference following multiple imputation for generalized additive models: an investigation of the median p-value rule with applications to the Pulmonary Hypertension Association Registry and Colorado COVID-19 hospitalization data

Preoperative glycaemic control, number of pain locations, structural knee damage, self-reported central sensitisation, satisfaction and personal control are predictive of 1-year postoperative pain, and change in pain from pre- to 1-year posttotal knee arthroplasty

PURPOSE

The aim of this study was to identify preoperative predictors for 1-year posttotal knee arthroplasty (TKA) pain and pre- to post-TKA pain difference in knee osteoarthritis (KOA) patients.

METHODS

From March 2018 to July 2023, this prospective longitudinal cohort study enrolled KOA patients awaiting TKA from four hospitals in Belgium and the Netherlands. Different biopsychosocial predictors were assessed preoperatively by questionnaires and physical examinations (input variables). The Knee injury and Osteoarthritis Outcome Score (KOOS) subscale pain was used to measure pain intensity. The absolute KOOS subscale pain score 1-year post-TKA and the difference score (ΔKOOS = 1-year postoperative - preoperative) were used as primary outcome measures (output variables). Two multivariable linear regression analyses were performed.

RESULTS

Two hundred and twenty-three participants were included after multiple imputation. Worse absolute KOOS subscale pain scores 1-year post-TKA and negative or closer to zero ΔKOOS subscale pain scores were predicted by self-reported central sensitisation, lower KOA grade and preoperative satisfaction, and higher glycated haemoglobin, number of pain locations and personal control (adjusted R2 = 0.25). Additional predictors of negative or closer to zero ΔKOOS subscale pain scores were being self-employed, higher preoperative pain and function (adjusted R2 = 0.37).

CONCLUSION

This study reports different biopsychosocial predictors for both outcomes that have filtered out other potential predictors and provide value for future studies on developing risk assessment tools for the prediction of chronic TKA pain.

PROTOCOL REGISTRATION

The protocol is registered at clinicaltrials.gov (NCT05380648) on 13 May 2022.

LEVEL OF EVIDENCE

Level II.

Preliminary comparison of net gain in final adult height of girls with early menarche treated with or without gonadotropin-releasing hormone agonist

Background

Early menarche is associated with both physical and psychosocial problems. Based on psychological and physical health considerations, for girls with early menarche, some parents and physicians may elect to use gonadotropin-releasing hormone agonists (GnRHa) to delay menstruation. This study aimed to explore the effects of GnRHa treatment on the final height of girls with early menarche and build the models to predict the final adult height (FAH).

Methods

Girls who experienced menarche between the ages of 8 and 10 years and were diagnosed with idiopathic central precocious puberty (ICPP) at Tianjin Medical University General Hospital between July 2017 and August 2023 were included in this study. Participants were divided into two groups based on treatment strategy: GnRHa-treated and GnRHa-untreated groups. Laboratory parameters including growth factors and basal gonadotropins were tested at diagnosis. The heights and weights of the participants were measured every three months. Bone radiographs of the left hand and wrist were assessed by professional appraisers to determine the bone age (BA), which was measured every 6 months after diagnosis.

Results

Clinical data of 176 girls who experienced early menarche were retrospectively analyzed. For participants in the GnRHa-treated group (n=87), growth velocity (GV) showed significant differences between the first 6 months and second 6 months of treatment (P=0.01; 5.82±2.3 vs. 4.79±2.31 cm, respectively). The height standard deviation score (SDS) and BA (P<0.001) decreased during treatment. The predicted adult height was higher at the end of treatment, but was not statistically different from that at diagnosis (P=0.73). In the linear regression analysis, no significant relationships were observed between GnRHa treatment and net gain (NG) in final height [Model A, adjusted for BA and chronological age (CA) at baseline: P=0.43; Model B, adjusted for Model A plus HtSDS-BA, HtSDS, and BMISDS: P=0.65; Model C, adjusted for Model B plus LH, FSH, and IGF-1: P=0.82]. The generalized additive model (GAM) for NG in final height in GnRHa-treated participants included three independent risk factors: LH/FSH [estimated degrees of freedom (edf) =5.36, P=0.02], GV (edf =4.11, P=0.007), and the bone maturation ratio (BMR) (edf =4.79, P=0.02). GAM performed better than multivariate linear (stepwise) regression in predicting the FAH in GnRHa-treated girls with early menarche.

Conclusions

For girls who experienced menarche between the ages of 8 and 10 years, GnRHa treatment suppressed GV and skeletal maturity. The GAM provides a theoretical basis for pediatric endocrinologists in deciding whether to apply GnRHa treatment, determining the time to withdraw GnRHa treatment, and predicting the FAH of girls with early menarche.

An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients

Appendicitis, an infection and inflammation of the appendix is a prevalent condition in children that requires immediate treatment. Rupture of the appendix may lead to several complications, such as peritonitis and sepsis. Appendicitis is medically diagnosed using urine, blood, and imaging tests. In recent times, Artificial Intelligence and machine learning have been a boon for medicine. Hence, several supervised learning techniques have been utilized in this research to diagnose appendicitis in pediatric patients. Six heterogeneous searching techniques have been used to perform hyperparameter tuning and optimize predictions. These are Bayesian Optimization, Hybrid Bat Algorithm, Hybrid Self-adaptive Bat Algorithm, Firefly Algorithm, Grid Search, and Randomized Search. Further, nine classification metrics were utilized in this study. The Hybrid Bat Algorithm technique performed the best among the above algorithms, with an accuracy of 94% for the customized APPSTACK model. Five explainable artificial intelligence techniques have been tested to interpret the results made by the classifiers. According to the explainers, length of stay, means vermiform appendix detected on ultrasonography, white blood cells, and appendix diameter were the most crucial markers in detecting appendicitis. The proposed system can be used in hospitals for an early/quick diagnosis and to validate the results obtained by other diagnostic modalities.

Exploring the Interactions Between Psychotic Symptoms, Cognition, and Environmental Risk Factors: A Bayesian Analysis of Networks

BACKGROUND AND HYPOTHESIS

Psychotic disorders (PDs) have huge personal and societal impact, and efforts to improve outcomes in patients are continuously needed. Environmental risk factors (ERFs), especially modifiable risk factors, are important to study because they pose a target for intervention and prevention. No studies have investigated ERFs, cognition, and psychotic symptoms together in a network approach.

STUDY DESIGN

We explored interactions between 3 important ERFs (tobacco smoking, cannabis use, and childhood trauma), 6 cognitive domains, and 3 dimensions of symptoms in psychosis. From the Genetic Risk and Outcome of Psychosis (GROUP) cohort, we used data from patients, siblings, and healthy controls to construct networks using Bayesian analyses of all 12 variables. We constructed networks of the combined sample and of patients and siblings separately.

STUDY RESULTS

We found that tobacco smoking was directly associated with cognition and psychotic symptoms. The cognitive variable processing speed was the most central node, connecting clusters of psychotic symptoms and substance use through the variables of positive symptoms and tobacco smoking. Comparing the networks of patients and siblings, we found that networks were relatively similar between patients and siblings.

CONCLUSIONS

Our results support a potential central role of processing speed deficits in PDs. Findings highlight the importance of integrating tobacco smoking as potential ERFs in the context of PDs and to broaden the perspective from cannabis discontinuation to smoking cessation programs in patients or people at risk of PDs.

Application of the IASP Grading System to Identify Underlying Pain Mechanisms in Patients With Knee Osteoarthritis: A Prospective Cohort Study

OBJECTIVES

This study aimed to apply the International Association for the Study of Pain (IASP) grading system for identifying nociplastic pain in knee osteoarthritis (KOA) awaiting total knee arthroplasty (TKA) and propose criteria to fine-tune decision-making. In addition, the study aimed to characterize a "probable" versus "no or possible" nociplastic pain mechanism using biopsychosocial variables and compare both groups in their 1-year post-TKA response.

METHODS

A secondary analysis of baseline data of a longitudinal prospective study involving 197 patients with KOA awaiting total TKA in Belgium and the Netherlands was performed. Two approaches, one considering 4 and the other 3 pain locations (step 2 of the grading system), were presented. Linear mixed model analyses were performed to compare the probable and no or possible nociplastic pain mechanism groups for several preoperative biopsychosocial-related variables and 1-year postoperative pain. Also, a sensitivity analysis, comparing 3 pain mechanism groups, was performed.

RESULTS

Thirty (15.22%-approach 4 pain locations) and 46 (23.35%-approach 3 pain locations) participants were categorized under probable nociplastic pain. Irrespective of the pain location approach or sensitivity analysis, the probable nociplastic pain group included more woman, was younger, exhibited worse results on various preoperative pain-related and psychological variables, and had more pain 1-year post-TKA compared with the other group.

DISCUSSION

This study proposed additional criteria to fine-tune the grading system for nociplastic pain (except for discrete/regional/multifocal/widespread pain) and characterized a subgroup of patients with KOA with probable nociplastic pain. Future research is warranted for further validation.

Sex differences in the role of sleep on cognition in older adults

Study Objectives

The study aimed to investigate sex differences in the relationship between sleep quality (self-report and objective) and cognitive function across three domains (executive function, verbal memory, and attention) in older adults.

Methods

We analyzed cross-sectional data from 207 participants with normal cognition (NC) or mild cognitive impairment (89 males and 118 females) aged over 60 years. The relationship between sleep quality and cognitive performance was estimated using generalized additive models. Objective sleep was measured with the GT9X Link ActiGraph, and self-reported sleep was measured with the Pittsburgh Sleep Quality Index.

Results

We found that females exhibited lower executive function with increased objective total sleep time, with a steeper decline in performance after 400 minutes (p = .015). Additionally, longer objective sleep correlated with lower verbal memory linearly (p = .046). In males, a positive linear relationship emerged between objective sleep efficiency and executive function (p = .036). Self-reported sleep was not associated with cognitive performance in females and males with NC. However, in males with cognitive impairment, there was a nonlinear positive relationship between self-reported sleep and executive function (p < .001).

Conclusions

Our findings suggest that the association between sleep parameters on cognition varies between older males and females, with executive function being most strongly associated with objective sleep for both sexes top of form. Interventions targeting sleep quality to mitigate cognitive decline in older adults may need to be tailored according to sex, with distinct approaches for males and females.

Scanpro is a tool for robust proportion analysis of single-cell resolution data

In higher organisms, individual cells respond to signals and perturbations by epigenetic regulation and transcriptional adaptation. However, in addition to shifting the expression level of individual genes, the adaptive response of cells can also lead to shifts in the proportions of different cell types. Recent methods such as scRNA-seq allow for the interrogation of expression on the single-cell level, and can quantify individual cell type clusters within complex tissue samples. In order to identify clusters showing differential composition between different biological conditions, differential proportion analysis has recently been introduced. However, bioinformatics tools for robust proportion analysis of both replicated and unreplicated single-cell datasets are critically missing. In this manuscript, we present Scanpro, a modular tool for proportion analysis, seamlessly integrating into widely accepted frameworks in the Python environment. Scanpro is fast, accurate, supports datasets without replicates, and is intended to be used by bioinformatics experts and beginners alike.

Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies

The use of digital devices to collect data in mobile health studies introduces a novel application of time series methods, with the constraint of potential data missing at random or missing not at random (MNAR). In time-series analysis, testing for stationarity is an important preliminary step to inform appropriate subsequent analyses. The Dickey-Fuller test evaluates the null hypothesis of unit root non-stationarity, under no missing data. Beyond recommendations under data missing completely at random for complete case analysis or last observation carry forward imputation, researchers have not extended unit root non-stationarity testing to more complex missing data mechanisms. Multiple imputation with chained equations, Kalman smoothing imputation, and linear interpolation have also been used for time-series data, however such methods impose constraints on the autocorrelation structure and impact unit root testing. We propose maximum likelihood estimation and multiple imputation using state space model approaches to adapt the augmented Dickey-Fuller test to a context with missing data. We further develop sensitivity analyses to examine the impact of MNAR data. We evaluate the performance of existing and proposed methods across missing mechanisms in extensive simulations and in their application to a multi-year smartphone study of bipolar patients.

Impact of the COVID-19 pandemic on chronic disease management and patient reported outcomes in patients with pulmonary hypertension: The Pulmonary Hypertension Association Registry

To better understand the impact of the COVID-19 pandemic on the care of patients with pulmonary hypertension, we conducted a retrospective cohort study evaluating health insurance status, healthcare access, disease severity, and patient reported outcomes in this population. Using the Pulmonary Hypertension Association Registry (PHAR), we defined and extracted a longitudinal cohort of pulmonary arterial hypertension (PAH) patients from the PHAR's inception in 2015 until March 2022. We used generalized estimating equations to model the impact of the COVID-19 pandemic on patient outcomes, adjusting for demographic confounders. We assessed whether insurance status modified these effects via covariate interactions. PAH patients were more likely to be on publicly-sponsored insurance during the COVID-19 pandemic compared with prior, and did not experience statistically significant delays in access to medications, increased emergency room visits or nights in the hospital, or worsening of mental health metrics. Patients on publicly-sponsored insurance had higher healthcare utilization and worse objective measures of disease severity compared with privately insured individuals irrespective of the COVID-19 pandemic. The relatively small impact of the COVID-19 pandemic on pulmonary hypertension-related outcomes was unexpected but may be due to pre-established access to high quality care at pulmonary hypertension comprehensive care centers. Irrespective of the COVID-19 pandemic, patients who were on publicly-sponsored insurance seemed to do worse, consistent with prior studies highlighting outcomes in this population. We speculate that previously established care relationships may lessen the impact of an acute event, such as a pandemic, on patients with chronic illness.