Researchers at Saratoga Hospital in Saratoga Springs, New York, report that a pooled testing strategy for emergency patients at low risk for COVID-19 enabled the hospital to test 530 patients using just 179 Cepheid Xpert Xpress SARS-CoV-2 test cartridges, 340 fewer cartridges than if each sample had been tested separately (J Hosp Med 2020; doi:10.12788/jhm.3501). According to the authors, this strategy “conserved [personal protective equipment], led to a marked reduction in staff and patient anxiety, and improved patient care.”
As a 171-bed community institution, Saratoga Hospital sought to perform SARS-CoV-2 testing on all patients being admitted or under observation for admission. Emergency department staff classified patients as high- or low-risk for having COVID-19 illness based on history, clinical presentation, and other diagnostic results. Samples from high-risk patients were sent out for SARS-CoV-2 polymerase chain reaction testing; rapid testing was used for samples from low-risk patients.
The investigators settled on a three-sample pooling strategy primarily because this size pool was appropriate given the 8% positivity rate among symptomatic outpatients at that time. Larger pools would have required more repeat testing whenever there was a positive result. In addition, this strategy would enable the hospital even with the limited test supplies to continue testing all patients who were being admitted. Finally holding testing for just three patients was “manageable” given the hospital’s rate of admissions and emergency volume. On select occasions, pools of samples from only two patients were used.
The authors validated pooled testing using samples from admitted patients with previously known positive or negative results that had been tested at the New York State Department of Health Wadsworth Center laboratory.
There were four positive pooled tests, which required using 11 additional cartridges for a positive rate of 0.8%. No patients in the negative pools developed symptoms of COVID-19 or tested positive while in the hospital. The authors concluded that pooled testing “is a valuable tool during a time of limited resources that may have application in testing other low-risk groups, including healthcare workers and clients of occupational medicine services.”
Serum Parathyroid Hormone Testing at VHA Falls Short in Workup of Suspected Primary Hyperparathyroidism
An analysis of data from the Veterans Health Administration (VHA) found that just one-quarter of patients with both hypercalcemia and kidney stones had their serum parathyroid hormone (PTH) levels measured even though professional guidelines recommend this testing in patients with kidney stones suspected of having primary hyperparathyroidism (JAMA Surg 2020; doi:10.1001/jamasurg.2020.2423). The authors concluded, “Improved screening for [primary hyperparathyroidism] could increase the rates of detection and treatment of [this disease] and decrease stone recurrence associated with missed or untreated [primary hyperparathyroidism].
About half of patients with kidney stones develop a recurrent stone within 10 years. Patients with kidney stones and primary hyperparathyroidism typically have both hypercalcemia and hypercalciuria, which raise the risk for more stones. Checking serum calcium levels and serum PTH in patients with kidney stones could guide whether parathyroidectomy would be indicated in those suspected of primary hyperparathyroidism, thereby reducing risk of more stones.
From a total of 157,539 veterans who had kidney stones over a 5-year period, the researchers identified 7,561 who had a serum calcium test within 6 months of their index stone, had not had a serum PTH test from 6 months to 30 months before the index stone, and who did not have a serum calcium level <10.5 mg/dL. Of these, 24.8% had a serum PTH assessment. However, testing rates varied across 130 VHA institutions from 4% to 57%.
Key variables associated with PTH testing included estimated glomerular filtration rate <45 mL/min/1.73 m2 (1.52 odds ratio (OR), P value <.001), albumin-corrected versus measured hypercalcemia (.32 OR, P value <.001), and a patient having seen both a nephrologist and urologist or an endocrinologist (6.57 OR, P value <.001; 4.83 OR, P value <.001, respectively).
The authors of an associated commentary called for a system-level solution to improve utilization of PTH measurements and suggested that one might be to develop an automatic electronic health record alert prompting PTH testing for all kidney stone patients who also have hypercalcemia (JAMA Surg 2020; doi:10.1001/jamasurg.2020.2448).
MALDI-MS With Machine Learning Method for Identifying SARS-CoV-2 Proposed as Promising Alternative to RT-PCR
Researchers at Autonomous University of Chile and University of Talca in Talca, Chile, describe using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis to detect SARS-CoV-2 with up to 94.7% sensitivity and 92.6% specificity (Nature Biotechnol 2020; doi.org/10.1038/s41587-020-0644-7).
MALDI-MS, the investigators contend, is “a promising alternative” to reverse transcriptase-polymerase chain reaction (PCR) as a “fast screening assay” for SARS-CoV-2. This method could be particularly useful in developing countries “given its speed, simplicity, and low cost, and the availability of equipment and expertise in many hospital laboratories in [those] countries” wrote the researchers.
According to the investigators, MALDI-MS is used for routine diagnostics of yeast and bacterial infections, but no spectral libraries exist to identify SARS-CoV-2. They used samples from 362 patients to develop these spectra (211 PCR-confirmed positive, 151 negative). The researchers deployed feature selection methods to select the most characteristic peaks for distinguishing SARS-CoV-2-positive from SARS-CoV-2-negative samples.
These data then informed principal component analysis to explore and compare the spectra using peaks selected by feature selection methods. Finally, the researchers deployed six machine learning algorithms; the support vector machine with a radial kernel model with no feature selection achieved the highest accuracy.
Nader Rifai, PhD, DABCC, FAACC, is a professor of pathology at Harvard Medical School in Boston and Louis Joseph Gay-Lussac Chair in laboratory medicine and director of clinical chemistry at Boston Children’s Hospital.