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Predicting ADHD symptoms and diagnosis at age 14 from objective activity levels at age 7 in a large UK cohort.

ABSTRACT: Hyperactivity is one of the three core symptoms in children with attention deficit hyperactivity disorder (ADHD). Diagnosing ADHD typically involves self-report, third party report and observations. Objective activity data can make a valuable contribution to the diagnostic process. Small actigraphy studies in clinical samples have shown that children with ADHD move more than children without ADHD. However, differences in physical activity between children with and without ADHD have not been assessed in large community samples or longitudinally. This study used data from the Millennium Cohort Study to test whether symptoms of ADHD (parent-rating Strengths and Difficulties Questionnaire) and ADHD diagnosis at age 14 (reported by parents) could be predicted from objective activity data (measured with actigraphs) at age 7 in N = 6675 children (final N = 5251). Regressions showed that less sedentary behavior at age 7 predicted more ADHD symptoms at age 14 (β = - 0.002, CI - 0.004 to - 0.001). The result remained significant when controlled for ADHD symptoms at age 7, sex, BMI, month of birth, SES and ethnicity (β = - 0.001, CI - 0.003 to - 0.0003). ADHD diagnosis at age 14 was also significantly predicted by less sedentary behavior at age 7 (β = - 0.008). Our findings show that symptoms of ADHD can be predicted by objective activity data 5 years in advance and suggest that actigraphy could be a useful instrument aiding an ADHD diagnosis. Interestingly, the results indicate that the key difference between children with and without ADHD lies in reduced sedentary activity, i.e., times of rest.

SUBMITTER: Brandt V

PROVIDER: S-EPMC8140967 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundElderly patients are at particular risk for bacteremia and sepsis. Atypical presentation may complicate the diagnosis. We studied patients with bacteremia, in order to assess possible age-related effects on the clinical presentation and course of severe infections.MethodsWe reviewed the records of 680 patients hospitalized between 1994 and 2004. All patients were diagnosed with bacteremia, 450 caused by Escherichia coli and 230 by Streptococcus pneumoniae. Descriptive analyses were performed for three age groups (<?65 years, 65-84 years, ??85 years). In multivariate analyses age was dichotomized (<?65, ??65 years). Symptoms were categorized into atypical or typical. Prognostic sensitivity of CRP and SIRS in identifying early organ failure was studied at different cut-off values. Outcome variables were organ failure within one day after admission and in-hospital mortality.ResultsThe higher age-groups more often presented atypical symptoms (p <0.001), decline in general health (p=0.029), and higher in-hospital mortality (p<0.001). The prognostic sensitivity of CRP did not differ between age groups, but in those ??85 years the prognostic sensitivity of two SIRS criteria was lower than that of three criteria. Classical symptoms were protective for early organ failure (OR 0.67, 95% CI 0.45-0.99), and risk factors included; age ??65 years (OR 1.65, 95% CI 1.09-2.49), comorbid illnesses (OR 1.19, 95% CI 1.02-1.40 per diagnosis), decline in general health (OR 2.28, 95% CI 1.58-3.27), tachycardia (OR 1.50, 95% CI 1.02-2.20), tachypnea (OR 3.86, 95% CI 2.64-5.66), and leukopenia (OR 4.16, 95% CI 1.59-10.91). Fever was protective for in-hospital mortality (OR 0.46, 95% CI 0.24-0.89), and risk factors included; age ??65 years (OR 15.02, 95% CI 3.68-61.29), ??1 comorbid illness (OR 2.61, 95% CI 1.11-6.14), bacteremia caused by S. pneumoniae (OR 2.79, 95% CI 1.43-5.46), leukopenia (OR 4.62, 95% CI 1.88-11.37), and number of early failing organs (OR 3.06, 95% CI 2.20-4.27 per failing organ).ConclusionsElderly patients with bacteremia more often present with atypical symptoms and reduced general health. The SIRS-criteria have poorer sensitivity for identifying organ failure in these patients. Advanced age, comorbidity, decline in general health, pneumococcal infection, and absence of classical symptoms are markers of a poor prognosis.

Project description:Recently, there has been an increase in telemedicine applied to child neuropsychiatry, such as the use of online platforms to collect remotely case histories and demographic and behavioral information. In the present proof-of-concept study, we aimed to understand to what extent information parents and teachers provide through online questionnaires overlaps with clinicians' diagnostic conclusions on attention-deficit/hyperactivity disorder (ADHD). Moreover, we intended to explore a possible role that autism spectrum disorders (ASD) symptoms played in this process. We examined parent- and teacher-rated questionnaires collected remotely and an on-site evaluation of intelligence quotients from 342 subjects (18% females), aged 3-16 years, and referred for suspected ADHD. An easily interpretable machine learning model-decision tree (DT)-was built to simulate the clinical process of classifying ADHD/non-ADHD based on collected data. Then, we tested the DT model's predictive accuracy through a cross-validation approach. The DT classifier's performance was compared with those that other machine learning models achieved, such as random forest and support vector machines. Differences in ASD symptoms in the DT-identified classes were tested to address their role in performing a diagnostic error using the DT model. The DT identified the decision rules clinicians adopt to classify an ADHD diagnosis with an 82% accuracy rate. Regarding the cross-validation experiment, our DT model reached a predictive accuracy of 74% that was similar to those of other classification algorithms. The caregiver-reported ADHD core symptom severity proved the most discriminative information for clinicians during the diagnostic decision process. However, ASD symptoms were a confounding factor when ADHD severity had to be established. Telehealth procedures proved effective in obtaining an automated output regarding a diagnostic risk, reducing the time delay between symptom detection and diagnosis. However, this should not be considered an alternative to on-site procedures but rather as automated support for clinical practice, enabling clinicians to allocate further resources to the most complex cases.

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Predicting ADHD symptoms and diagnosis at age 14 from objective activity levels at age 7 in a large UK cohort.

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