A novel systematic pipeline for increased predictability and explainability of growth patterns in children using trajectory features

OBJECTIVE

Longitudinal patterns of growth in early childhood are associated with health conditions throughout life. Knowledge of such patterns and the ability to predict them can lead to better prevention and improved health promotion in adulthood. However, growth analyses are characterized by significant variability, and pattern detection is affected by the method applied. Moreover, pattern labelling is typically performed based on ad hoc methods, such as visualizations or clinical experience. Here, we propose a novel pipeline using features extracted from growth trajectories using mathematical, statistical and machine-learning approaches to predict growth patterns and label them in a systematic and unequivocal manner.

METHODS

We extracted mathematical and clinical features from 9577 children growth trajectories embedded with machine-learning predictions of the growth patterns. We experimented with two sets of features (CAnonical Time-series Characteristics and trajectory features specific to growth), developmental periods and six machine-learning classifiers. Clinical experts provided labels for the detected patterns and decision rules were created to associate the features with the labelled patterns. The predictive capacity of the extracted features was validated on two heterogenous populations (The Applied Research Group for Kids and the 2004 Pelotas Birth Cohort, based in Canada and Brazil, respectively).

RESULTS

Features predictive ability measured by accuracy and F1 score was ≥ 80% and ≥ 0.76 respectively in both cohorts. A small number of features (n = 74) was sufficient to distinguish between growth patterns in both cohorts. Slope, intercept of the trajectory, age at peak value, start value and change of the growth measure were among the top identified features.

CONCLUSION

Growth features can be reliably used as predictors of growth patterns and provide an unbiased understanding of growth patterns. They can be used as tool to reduce the effort to repeat analysis and variability concerning anthropometric measures, time points and analytical methods, in the context of the same or similar populations.

Osteoarticular mycobacterial infections in patients with the human immunodeficiency virus

OBJECTIVE

Data about the characteristics of patients with the human immunodeficiency virus (HIV) and concomitant mycobacterial skeletal infection are scarce. Thus, our aim was to describe this condition in a cohort of 11 patients.

METHODS

A review of the records of 11 HIV-positive individuals with microbiological confirmation of mycobacterial osteoarticular infection was conducted. The studied data included: age, sex, risk factor for the HIV days between the onset of symptoms and diagnosis, evidence of previous tuberculosis, location of the infection, isolated organism, diagnostic method, laboratory data (erythrocyte sedimentation rate, haemoglobin, leukocyte count), number of CD4+ lymphocytes, anti-retroviral therapy, treatment and outcome.

RESULTS

Eight patients were men and 3 were women. The median age was 34.2 years (range 20-46 years). Previous tuberculosis was present in 5 cases. Mean days between the onset of symptoms and diagnosis was 124 (range 20-365 days). Infections involved the knee (4 cases), spine (3 cases), hip (2 cases), elbow (1 case) and tibia (1 case). ESR was frequently elevated. The CD4 count ranged from 0.03 to 0.779 x 10(9)/l (mean 0.245 x 10(9)/l). M. tuberculosis was the responsible organism in 9 cases, Mycobacerium tuberculosis plus Staphylococcus aureus in one case and M. Kansasii in one case. Patients received specific treatments with good results. Surgery was necessary in 4 cases. No deaths occurred. Four patients were anti-retroviral naive at the moment the diagnosis was made. The remainder 8 were on zidovudine therapy.

CONCLUSION

The immunologic status of patients with HIV and concomitant mycobacterial skeletal infections is quite variable. The outcome of this condition seems to be good.