Does This Child With High Blood Pressure Have Secondary Hypertension?: The Rational Clinical Examination Systematic Review.

Importance: Guidelines recommend that all children and adolescents with hypertension undergo evaluation for secondary causes. Identifying clinical factors associated with secondary hypertension may decrease unnecessary testing for those with primary hypertension. Objective: To determine the utility of the clinical history, physical examination, and 24-hour ambulatory blood pressure monitoring for differentiating primary hypertension from secondary hypertension in children and adolescents (aged ≤21 years). Data Sources and Study Selection: The databases of MEDLINE, PubMed Central, Embase, Web of Science, and Cochrane Library were searched from inception to January 2022 without language limits. Two authors identified studies describing clinical characteristics in children and adolescents with primary and secondary hypertension. Data Extraction and Synthesis: For each clinical finding in each study, a 2 × 2 table was created that included the number of patients with and without the finding who had primary vs secondary hypertension. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Main Outcomes and Measures: Random-effects modeling was used to calculate sensitivity, specificity, and likelihood ratios (LRs). Results: Of 3254 unique titles and abstracts screened, 30 studies met inclusion criteria for the meta-analysis and 23 (N = 4210 children and adolescents) were used for pooling in the meta-analysis. In the 3 studies conducted at primary care clinics or school-based screening clinics, the prevalence of secondary hypertension was 9.0% (95% CI, 4.5%-15.0%). In the 20 studies conducted at subspecialty clinics, the prevalence of secondary hypertension was 44% (95% CI, 36%-53%). The demographic findings most strongly associated with secondary hypertension were family history of secondary hypertension (sensitivity, 0.46; specificity, 0.90; LR, 4.7 [95% CI, 2.9-7.6]), weight in the 10th percentile or lower for age and sex (sensitivity, 0.27; specificity, 0.94; LR, 4.5 [95% CI, 1.2-18]), history of prematurity (sensitivity range, 0.17-0.33; specificity range, 0.86-0.94; LR range, 2.3-2.8), and age of 6 years or younger (sensitivity range, 0.25-0.36; specificity range, 0.86-0.88; LR range, 2.2-2.6). Laboratory studies most associated with secondary hypertension were microalbuminuria (sensitivity, 0.13; specificity, 0.99; LR, 13 [95% CI, 3.1-53]) and serum uric acid concentration of 5.5 mg/dL or lower (sensitivity range, 0.70-0.73; specificity range, 0.65-0.89; LR range, 2.1-6.3). Increased daytime diastolic blood pressure load combined with increased nocturnal systolic blood pressure load on 24-hour ambulatory blood pressure monitoring was associated with secondary hypertension (sensitivity, 0.40; specificity, 0.82; LR, 4.8 [95% CI, 1.2-20]). Findings associated with a decreased likelihood of secondary hypertension were asymptomatic presentation (LR range, 0.19-0.36), obesity (LR, 0.34 [95% CI, 0.13-0.90]), and family history of any hypertension (LR, 0.42 [95% CI, 0.30-0.57]). Hypertension stage, headache, and left ventricular hypertrophy did not distinguish secondary from primary hypertension. Conclusions and Relevance: Family history of secondary hypertension, younger age, lower body weight, and increased blood pressure load using 24-hour ambulatory blood pressure monitoring were associated with a higher likelihood of secondary hypertension. No individual sign or symptom definitively differentiates secondary hypertension from primary hypertension.

Prevalence of Secondary Hypertension in Otherwise Healthy Youths with a New Diagnosis of Hypertension: A Meta-Analysis.

OBJECTIVE: To estimate the prevalence of secondary hypertension among otherwise healthy children with hypertension diagnosed in the outpatient setting. STUDY DESIGN: The MEDLINE, PubMed Central, Embase, Web of Science, and Cochrane Library databases were systematically searched for observational studies reporting the prevalence of secondary hypertension in children who underwent evaluation for hypertension and had no known comorbidities associated with hypertension at the time of diagnosis. Two authors independently extracted the study-specific prevalence of secondary hypertension in children evaluated for hypertension. Prevalence estimates for secondary hypertension were pooled in a random-effects meta-analysis. RESULTS: Nineteen prospective studies and 7 retrospective studies including 2575 children with hypertension were analyzed, with a median of 65 participants (range, 9-486) in each study. Studies conducted in primary care or school settings reported a lower prevalence of secondary hypertension (3.7%; 95% CI, 1.2%-7.2%) compared with studies conducted in referral clinics (20.1%; 95% CI, 11.5%-30.3%). When stratified by study setting, there were no significant subgroup differences according to study design, country, participant age range, hypertension definition, blood pressure device, or study quality. Although the studies applied different approaches to diagnosing secondary hypertension, diagnostic evaluations were at least as involved as the limited testing recommended by current guidelines. CONCLUSIONS: The low prevalence of secondary hypertension among children with a new diagnosis of hypertension identified on screening reinforces clinical practice guidelines to avoid extensive testing in the primary care setting for secondary causes in most children with hypertension.

Real-Time Prediction of Acute Kidney Injury in Hospitalized Adults: Implementation and Proof of Concept.

RATIONALE & OBJECTIVE: Acute kidney injury (AKI) is diagnosed based on changes in serum creatinine concentration, a late marker of this syndrome. Algorithms that predict elevated risk for AKI are of great interest, but no studies have incorporated such an algorithm into the electronic health record to assist with clinical care. We describe the experience of implementing such an algorithm. STUDY DESIGN: Prospective observational cohort study. SETTING & PARTICIPANTS: 2,856 hospitalized adults in a single urban tertiary-care hospital with an algorithm-predicted risk for AKI in the next 24 hours>15%. Alerts were also used to target a convenience sample of 100 patients for measurement of 16 urine and 6 blood biomarkers. EXPOSURE: Clinical characteristics at the time of pre-AKI alert. OUTCOME: AKI within 24 hours of pre-AKI alert (AKI24). ANALYTICAL APPROACH: Descriptive statistics and univariable associations. RESULTS: At enrollment, mean predicted probability of AKI24 was 19.1%; 18.9% of patients went on to develop AKI24. Outcomes were generally poor among this population, with 29% inpatient mortality among those who developed AKI24 and 14% among those who did not (P<0.001). Systolic blood pressure<100mm Hg (28% of patients with AKI24 vs 18% without), heart rate>100 beats/min (32% of patients with AKI24 vs 24% without), and oxygen saturation<92% (15% of patients with AKI24 vs 6% without) were all more common among those who developed AKI24. Of all biomarkers measured, only hyaline casts on urine microscopy (72% of patients with AKI24 vs 25% without) and fractional excretion of urea nitrogen (20% [IQR, 12%-36%] among patients with AKI24 vs 34% [IQR, 25%-44%] without) differed between those who did and did not develop AKI24. LIMITATIONS: Single-center study, reliance on serum creatinine level for AKI diagnosis, small number of patients undergoing biomarker evaluation. CONCLUSIONS: A real-time AKI risk model was successfully integrated into the EHR.

A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children.

BACKGROUND: Timely prediction of AKI in children can allow for targeted interventions, but the wealth of data in the electronic health record poses unique modeling challenges. METHODS: We retrospectively reviewed the electronic medical records of all children younger than 18 years old who had at least two creatinine values measured during a hospital admission from January 2014 through January 2018. We divided the study population into derivation, and internal and external validation cohorts, and used five feature selection techniques to select 10 of 720 potentially predictive variables from the electronic health records. Model performance was assessed by the area under the receiver operating characteristic curve in the validation cohorts. The primary outcome was development of AKI (per the Kidney Disease Improving Global Outcomes creatinine definition) within a moving 48-hour window. Secondary outcomes included severe AKI (stage 2 or 3), inpatient mortality, and length of stay. RESULTS: Among 8473 encounters studied, AKI occurred in 516 (10.2%), 207 (9%), and 27 (2.5%) encounters in the derivation, and internal and external validation cohorts, respectively. The highest-performing model used a machine learning-based genetic algorithm, with an overall receiver operating characteristic curve in the internal validation cohort of 0.76 [95% confidence interval (CI), 0.72 to 0.79] for AKI, 0.79 (95% CI, 0.74 to 0.83) for severe AKI, and 0.81 (95% CI, 0.77 to 0.86) for neonatal AKI. To translate this prediction model into a clinical risk-stratification tool, we identified high- and low-risk threshold points. CONCLUSIONS: Using various machine learning algorithms, we identified and validated a time-updated prediction model of ten readily available electronic health record variables to accurately predict imminent AKI in hospitalized children.