Context statements are not required, but one or more context statements may be used within a library to help clarify how the CQL expressions will be interpreted. Patient context is the default if none is specified. Replaced measure-defined definitions with similar definitions and functions from CQL shared libraries for consistency across measures. Switching from QDM to CQL brings with it many changes, as well as enhanced expression capability, but only those changes with significant impact will be outlined in technical release notes.
For example, in the case of timing operators, changes may only be summarized if those changes impact the measure calculation. Value Set. Value set Office Visit 2. Value set Home Healthcare Services 2. Value set Pregnancy 2. Value set Payer 2. Value set Kidney Transplant Recipient 2. Value set Face-to-Face Interaction 2. A direct referenced code is a single concept code that is used to describe a clinical element directly within the logic.
The use of direct referenced codes replaces the need for single code value sets. Measures using other code systems in single value sets may optionally transition to direct referenced codes. Value set Vascular Access for Dialysis 2. Value set Kidney Transplant 2. Added 2 CPT codes , Controlling High Blood Pressure.
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Measure Description. Initial Population. Denominator Statement. Denominator Exclusions. Exclude patients whose hospice care overlaps the measurement period. Numerator Statement. Numerator Exclusions. Denominator Exceptions. Measure Steward. National Committee for Quality Assurance. Effective Clinical Care. We used Multivariate Poisson regression analysis to analyze variation in blood pressure measurement rates and associations with patient and physician factors.
Obesity or a recorded family history of hypertension were not associated with more blood pressure measurements. Interpretation: This screening measure was frequently done and appeared to be incompletely followed up.
Clear guidance is needed; guideline developers should consider reviewing this topic. Stable hypertension should be monitored at least every 6 months. Therefore, an abnormal measurement should lead to repeated measurement within a maximal interval of 6 months. American guidelines recommending the measurement of blood pressure for children between 3 and 18 years of age during every health care episode were published in 4 and endorsed in In , the United States Preventive Services Task Force issued an "I" insufficient evidence recommendation for screening for hypertension in children and adolescents.
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In Canada, the Greig Health Record for children and adolescents aged yr and the Rourke Baby Record for children aged yr have both incorporated periodic blood pressure measurement based on "Fair evidence. Owing to the lack of clear recommendations in the US and the absence of evidence-based Canadian guidelines, there is uncertainty about the need to measure blood pressure in asymptomatic children. We sought to provide evidence on current screening and follow-up that could be used by guideline authors to set priorities for review.
Our objectives were to describe hypertension screening and follow-up in children and adolescents in a Canadian urban primary care population sample. We determined current rates of hypertension screening in children and adolescents, the proportion of patients who receive timely follow-up after an initial abnormal blood pressure reading and the patient and provider characteristics associated with screening rates.
This was a retrospective cross-sectional observational study. Most providers were family physicians; 1 provider was a nurse practitioner. Both structured and free-text data are extracted. Extensive validation is done to ensure data extraction is complete and without error. Patients were randomly selected from the study dataset, their information was matched to their electronic charts front-end EMR , and data extraction queries were adjusted where needed.
We used data from the first quarter data extraction cycle for this study. Data extraction procedures have been described previously. The study population included all patients flagged as being enrolled in or rostered to the practice and indicated as being active nontransient in the EMR as of Apr.
All encounters recorded when a patient was between 3 and 18 years of age were extracted. Encounters recorded in the EMR can include phone or email encounters, which were excluded. We only included office visits, because there was an opportunity to measure blood pressure at the office during those encounters. The study sample included children and adolescents with a minimum of 2 office visits with at least 6 months between the first and last visit recorded in the EMR between the ages of 3 and 18 years.
Visits with blood pressure measurements were identified, and the patient's age at the time of visit was calculated. If more than 1 blood pressure record was available for a single date, we calculated the mean systolic and diastolic blood pressures for that visit. If height was not recorded for an encounter with recorded blood pressure, we used the closest height for the same age from other visits if done within 12 months of the encounter.
To determine whether a diagnosis of hypertension or a family history of hypertension was entered in the record, we searched each patient's summative health profile and encounter diagnosis records. The summative health profile contains summary data on chronic conditions, medications, allergies, immunizations, and social and family history. Each provider participating in the network completed a survey on their demographic and practice characteristics.
We used these data to describe providers. We calculated the proportion of the study sample that had at least 1 blood pressure measurement. For each visit with an available weight and height, we calculated sex and age-adjusted body mass index BMI centiles using the most recent World Health Organization growth charts. The outcome for the patient-level analysis was the count of the number of blood pressure measurements they had over their follow-up.
Multivariate Poisson regression analysis was used to analyze variation in blood pressure measurement rates. Patients were nested within primary care providers; we therefore applied the generalized estimating equation GEE method to take into account the correlation between patients clustered within providers. The number of visits that a patient can have is influenced by their duration under care as a patient for a given physician.
In this study, we calculated duration under care as the difference between the date of the first visit and the date of the last visit, or the date of data extraction whichever came first during the age range of interest. We used the logarithm of the duration under care as the offset in our Poisson regression models.
We used regular Poisson regression to fit a model to each unique provider i. Here, the outcome was a count of the number of blood pressure measurements performed by each provider. The offset was the number of patient-years follow-up per provider. We adjusted for overdispersion in these models by applying the Deviance scale correction factor to the estimated variance-covariance matrix of the regression coefficients. These 2 methods were used to model relations between patient and provider characteristics with rate of blood pressure measurement as the outcome. All tests were 2-tailed and a p value of less than 0.
Data were analyzed using SAS version 9. All participating primary care providers provided written informed consent for the collection and analysis of their EMR data. Provider characteristics are shown in Appendix 1 available at www. The most common rate of recording was every years. We found that In multivariate analyses, increases in rates of blood pressure measurements were associated with a greater number of encounters rate ratio [RR] 1.
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Neither obesity nor having a recorded family history of hypertension was associated with an increased rate of blood pressure recording. There was a small association between an increased number of encounters and higher rate of blood pressure screening; older age at first encounter and female sex were also associated with a greater rate of screening.
Female physicians recorded blood pressures more often; no other significant associations were found Table 3.
However, once an abnormal blood pressure was recorded, timely follow-up was uncommon, even when the patient subsequently visited the office. Known risk factors such as obesity, male sex or a family history of hypertension were not associated with more frequent blood pressure recording. As with other studies, difficulties in determining abnormal pediatric blood pressures may have led to errors in the recognition of elevated blood pressure, resulting in inconsistent follow-up of abnormal readings. Although male patients have higher rates of pediatric hypertension, 2 female patients were more likely to undergo screening in our study, perhaps owing to contraceptive prescribing; measuring blood pressure is recommended before starting the oral contraceptive pill.
Other than provider sex, provider factors such as practice size or proportion of children in the practice population were not associated with rate of screening for hypertension. Our study has several strengths. It reflects care in community-based primary care practices where children are usually seen for routine health services. We extracted data from several different EMR products, suggesting a variety of possible data entry processes in different applications.
Family physicians for this study were not selected randomly, and the sample may not be representative of the general pediatric population across Canada. A BMI record was missing more frequently among children with no blood pressure measurement recorded. Therefore, the finding of no association between BMI and blood pressure screening in this study should be treated with caution.
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We limited the analysis to patients with at least 2 visits to a family physician to exclude transient patients; this may introduce a selection bias, because children with more severe health conditions or more substantial risk factors may be followed in specialized settings. Alternatively, children with little perceived need for health care may not see a physician at all. We report on the most common and significant factors obesity or a family history of hypertension that affect the prevalence of this condition in children.
Other factors or co-morbidities, such as diabetes, can affect hypertension and should be examined in future studies. We found a low rate of follow-up blood pressure measurements; it would be interesting to know whether physicians were aware of blood pressure centiles in addition to crude measurements. This information was not available in patient electronic charts, and we do not know whether care providers have used other methods, outside of the EMR, to calculate blood pressure centiles.
Future studies could examine how much the lack of awareness or availability of blood pressure centiles contributes to the lack of follow-up in children with hypertension. In this population of children and adolescents, hypertension screening was common, but timely follow-up of abnormal blood pressures was infrequent.
Known risk factors such as obesity or a family history of hypertension were not associated with higher rates of screening. The issues highlighted in this study can be used by guideline authors to set priorities for review topics. Clearly, evidence-based guideline recommendations on pediatric hypertension screening and follow-up are needed.