42 The accuracy and clinical implications of point-of-care testing in children
Bibliographic record
Abstract
Abstract Primary Subject area Nephrology Background Point-of-care testing (POCT) is commonly used at our institution to gather data quickly for sick patients, including electrolytes, glucose, and hemoglobin. Serum electrolytes, hemoglobin, and glucose are the gold standard of testing, but the results often lag POCT by a significant time period. Management decisions are made on the results returned by POCT. Thus, it is imperative to determine the accuracy of POCT at our institution. Objectives Determining whether POCT is an accurate and clinically appropriate method to measure electrolytes, glucose, and hemoglobin compared to standard serum testing. Design/Methods This study retrospectively reviewed 128 consecutive patients either assessed in the emergency department or admitted prior to November 1, 2019 who had both POCT and serum electrolytes (+/- glucose, hemoglobin, and lactate) performed within 4 hours of each other. A sample size of 128 was required to determine a difference of 3 mmol/L in sodium for an effect size of 0.5, with 0.05 level of significance and 80% statistical power. Patient demographics and additional labs drawn within 4 hours of POCT were extracted. Paired t-tests were used to compare values between serum testing and POCT for each patient. Secondary kappa coefficient analyses were performed to look at agreement within clinically-determined normal ranges. POCT was performed on Radiometer ABL835 FLEX analyzers, and serum testing on Beckman Coulter DxC 800 analyzers. Results There were 56 males and 72 females; age range 0.01–17.93 years. There were statistically significant differences between POCT and serum values for all electrolytes and hemoglobin, with POCT over-estimating, but not for glucose (Table 1). Within clinically determined normal ranges, there was substantial agreement between POCT and serum potassium, glucose, and hemoglobin, and fair agreement for sodium and bicarbonate (Table 2). Conclusion Our study highlights the importance of verifying abnormal POCT electrolytes and hemoglobin with serum values. Even when POCT values are normal, clinically significant hyponatremia and hypokalemia may not be detected, and when abnormal, hypernatremia and hyperkalemia may be overestimated. In patients with dysnatremia, if diagnosed with serum sodium and monitored with POCT (or vice versa), there is potential for incorrect diagnosis and/or rate of correction with clinical impact. Thus, when following electrolytes and hemoglobin values in a patient, POCT and serum should not be interchanged, and if there is clinical suspicion for these to be abnormal, verification with serum is warranted. Our study is limited to the specific POCT analyzer used, and behaviour of another analyzer may be different.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".