User-friendly tools to identify elevated blood pressure in children
Bibliographic record
Abstract
Despite weak evidence of benefits, paediatricians are increasingly urged to screen children and adolescents for cardiovascular risk factors, including elevated blood pressure (BP). The United States (US) National Heart, Lung and Blood Institute, and the European Society of Hypertension recommend that children three years of age and older have their BP measured either at every medical encounter or annually (1,2). Screening for elevated BP is, however, not performed regularly in children. For instance, in a recent study conducted in the US, screening for elevated BP occurred in 35% of ambulatory paediatric visits (3). Even when BP is measured, hypertension is frequently underdiagnosed in children (4). On one hand, the low uptake of BP screening in children can be explained by physicians' low awareness of the screening recommendations and by the belief that elevated BP screening is not beneficial in children. If the latter is true, the low rate of screening is not of concern. There are indeed serious questions about the benefits and harms of elevated BP screening in children, notably because the absolute risk of cardiovascular diseases associated with a given level of BP in childhood and the long-term impact of anti-hypertensive treatment beginning in childhood are not known, and because no study has experimentally assessed the benefits and harms of BP screening in children (5). Consequently, the US Preventive Services Task Force does not endorse a specific recommendation for or against BP screening in children. On the other hand, if one assumes that elevated BP screening is beneficial in children, then the rate of screening should be improved. Identification of elevated BP is, however, difficult in children (6). One major difficulty is the numerous BP thresholds used to define elevated BP, which are sex, age and height specific (2,7). Accounting for both sexes, 17 age categories and seven height percentile categories, there are 476 specific thresholds for the 95th percentile of systolic and diastolic BP of children from one to 17 years of age. These numerous thresholds are difficult to use in practice and a limiting factor to increasing uptake of BP screening in children. Simple, user-friendly tools would be of great help to clinicians for identifying elevated BP in children. Several methods to ease the identification of elevated BP in children exist (Table 1) (8–11) and could improve the detection of elevated BP in children. For instance, Mitchell et al (11) produced a table with 10 BP thresholds, with increments of 5 mmHg, using three-year age categories, independent of sex or height. Implementation of this table in a university-based paediatric clinic led to a large increase in the identification of children with elevated BP. Tools to identify children with elevated blood pressure (BP) Tools to identify children with elevated blood pressure (BP) Because BP is related to height in children, an elevated BP to height ratio (BPHR) has recently been proposed as an indicator of elevated BP (12–16). In the current issue of the Journal, Guo et al (pages 65–69) assessed the performance of BPHR to identify children with elevated BP (17). In this study conducted in a rural area of China, 6837 children and adolescents five to 18 years of age had their BP measured twice on one occasion. Elevated BP was defined as either systolic BP or diastolic BP at or above the 95th sex-, age- and height-specific percentile (US reference). Both systolic BPHR and diastolic BPHR accurately identified elevated BP: the areas under ROC curves of systolic BPHR and diastolic BPHR were between 0.94 and 0.99 for elevated systolic BP and elevated diastolic BP. The optimal cut-off points for children five to 12 years of age were 0.84 mmHg/cm for systolic BPHR and 0.54 mmHg/cm for diastolic BPHR in boys and 0.83 mmHg/cm and 0.53 mmHg/cm in girls, respectively. For children 13 to 18 years of age, the optimal cut-off points were 0.77 mmHg/cm for systolic BPHR and 0.48 mmHg/cm for diastolic BPHR in boys and 0.79 mmHg/cm and 0.51 mmHg/cm in girls, respectively (17). Studies conducted in other populations have also shown the ability of BPHR to identify subjects with elevated BP, in both sexes, in children as well as in adolescents (12–16). Nevertheless, it is too early to adopt BPHR for the screening of elevated BP. Optimal cut-off points have to be determined and tested in various populations. Their positive and negative predictive values for the detection of elevated BP have to be determined. Because BPHR could have a residual relationship with height, it is necessary to assess whether such predictive values are the same across height categories. Furthermore, it is unclear what the role of BPHR would be in the process from BP screening to the diagnosis of hypertension. It is also critical to assess whether the use of BPHR improves screening uptake and detection of elevated BP in various clinical settings. In practice, even if a simple and valid set of cut-off points are identified, using BPHR will be difficult because it requires computing a ratio at the point of care. Meanwhile, clinicians can avoid unfriendly BP tables and determine BP percentiles in children by using applications for smartphones (18) or online calculators (19).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.024 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".