Joint modelling of longitudinal 3MS scores and the risk of mortality among cognitively impaired individuals

Serial gait speed measurements over time and dynamic survival prediction in older adults

BACKGROUND

A one-time gait speed measurement predicts mortality risk. A framework for updating a clinician's mortality risk perception with new information from each clinic visit is needed. We used joint modeling of longitudinal and survival data for dynamic prediction of mortality risk.

METHODS

We fit sex-stratified joint models to 20-meter (bi)annual longitudinal gait speed measured every 6 months and 14-year survival data from the Health, Aging and Body Composition Study allowing for non-linear fluctuations of gait speed and controlling for important covariates such as age, recent hospitalization, blood pressure, obesity, and comorbidities.

RESULTS

Participants (N = 3048) were 74 years old with gait speed 1.18 m/s. They were 42% Black, and 52% died over 14 years. Higher gait speed of 0.1 m/s was associated with 23% (95% confidence interval or CI = 20-25%) and 25% (CI = 21-28%) reductions in mortality risk in men and women; and a 0.05 m/s annualized slowing (slope) with 31% (CI = 13-51%) increase in men (all p < 0.05), with findings persisting after covariate adjustment. Distant gait speed history over a year prior contributed little for mortality risk prediction with mean change of only 1-2% in 5-year risk.

CONCLUSION

The two most recent gait speeds appear sufficient to consider for mortality risk in the present initial analysis. More frequent gait speeds need to be considered in mortality risk prediction before definitive conclusions supporting real-world application.

Longitudinal-Survival Models for Case-Based Tuberculosis Progression

Introduction: Tuberculosis (TB) disease continues to be responsible for a high global burden with an estimated 10 million people falling ill each year and an estimated 1.45 million deaths. Widely carried out analyses to utilize routine data coming from this disease, and well-established in literature, have paid attention to time-to-event with sputum smear results being considered only at baseline or even ignored. Also, logistic regression models have been used to demonstrate importance of sputum smear results in patient outcomes. A feature presented by this disease, however, is that each individual patient is usually followed over a period of time with sputum smear results being documented at different points of the treatment curve. This provides both repeated measures and survival times, which may require a joint modeling approach. This study aimed to investigate the association between sputum smear results and the risk of experiencing unfavorable outcome among TB patients and dynamically predict survival probabilities.

Method: A joint model for longitudinal and time-to-event data was used to analyze longitudinally measured smear test results with time to experiencing unfavorable outcome for TB patients. A generalized linear mixed-effects model was specified for the longitudinal submodel and cox proportional hazards model for the time-to-event submodel with baseline hazard approximated using penalized B-splines. The two submodels were then assumed to be related via the current value association structure. Bayesian approach was used to approximate parameter estimates using Markov Chain Monte Carlo (MCMC) algorithm. The obtained joint model was used to predict the subject's future risk of survival based on sputum smear results trajectories. Data were sourced from routinely collected TB data stored at National TB Program database.

Results: The average baseline age was 35 (SD: 15). Female TB patients constituted 36.42%. Patients with previous history of TB treatment constituted 6.38% (event: 15.25%; no event: 5.29%). TB/HIV co-infection was at 31.23% (event: 47.87%; no event: 29.20%). The association parameter 1.03 (CI[1.03,1.04]) was found to be positive and significantly different from zero, interpreted as follows: The estimate of the association parameter α = 1.033 denoted the log hazard ratio for a unit increase in the log odds of having smear positive results. HIV status (negative) 0.47 (CI [0.46,49]) and history of TB treatment (previously treated) (2.52 CI [2.41,2.63]), sex (female) (0.82 CI [0.78,0.84]), and body mass index (BMI) categories (severe malnutrition being reference) were shown to be statistically significant.

Conclusion: Sputum smear result is important in estimating the risk to unfavorable outcome among TB patients. Men, previously treated, TB/HIV co-infected and severely malnourished TB patients are at higher risk of unfavorable outcomes.

Bayesian joint modelling of longitudinal and time to event data: a methodological review

BACKGROUND

In clinical research, there is an increasing interest in joint modelling of longitudinal and time-to-event data, since it reduces bias in parameter estimation and increases the efficiency of statistical inference. Inference and prediction from frequentist approaches of joint models have been extensively reviewed, and due to the recent popularity of data-driven Bayesian approaches, a review on current Bayesian estimation of joint model is useful to draw recommendations for future researches.

METHODS

We have undertaken a comprehensive review on Bayesian univariate and multivariate joint models. We focused on type of outcomes, model assumptions, association structure, estimation algorithm, dynamic prediction and software implementation.

RESULTS

A total of 89 articles have been identified, consisting of 75 methodological and 14 applied articles. The most common approach to model the longitudinal and time-to-event outcomes jointly included linear mixed effect models with proportional hazards. A random effect association structure was generally used for linking the two sub-models. Markov Chain Monte Carlo (MCMC) algorithms were commonly used (93% articles) to estimate the model parameters. Only six articles were primarily focused on dynamic predictions for longitudinal or event-time outcomes.

CONCLUSION

Methodologies for a wide variety of data types have been proposed; however the research is limited if the association between the two outcomes changes over time, and there is also lack of methods to determine the association structure in the absence of clinical background knowledge. Joint modelling has been proved to be beneficial in producing more accurate dynamic prediction; however, there is a lack of sufficient tools to validate the prediction.

Association between Anemia and Cognitive Impairment among Elderly Patients with Heart Failure

Cognitive impairment is more prevalent in heart failure (HF) patients. Anemia can influence cognitive ability and is likely more prevalent in elderly patients with HF. However, there are limited data on the association of anemia with cognitive impairment in elderly HF patients. This study aimed to identify the association between anemia and cognitive impairment in elderly HF patients. This secondary data analysis included 181 patients aged 60 years or older with HF. Patients were categorized into an anemic or non-anemic group based on World Health Organization (WHO) criteria. We assessed the cognitive function using the Modified Mini-Mental State (3MS) at the time of enrollment. The prevalence of anemia and cognitive impairment in older patients with HF was the same at 35.4%. The main finding of the multiple logistic regression indicated that compared to a non-anemic status, anemia increased the risk of cognitive impairment (odds ratio (OR) = 4.268, 95% confidence interval (CI) = 1.898–9.593, p < 0.001). Healthcare providers should recognize the value of the significance of early assessment of anemic status and cognitive function following HF. A prospective cohort study should identify the pathway of the association between anemia and incidence of cognitive impairment.