Implementation of INHERET, an Online Family History and Cancer Risk Interpretation Program for Primary Care and Specialty Clinics.

BACKGROUND: Individuals at increased risk for cancer are ascertained at low rates of 1% to 12% in primary care (PC). Underserved populations experience disparities of ascertainment, but data are lacking. INHERET is an online personal and family history tool to facilitate the identification of individuals who are eligible, according to guidelines, to be counseled on germline genetic testing and risk management. PATIENTS AND METHODS: INHERET data entry uses cancer genetics clinic questionnaires and algorithms that process patient data through NCCN Clinical Practice Guidelines in Oncology and best practice guidelines. The tool was tested in silico on simulated and retrospective patients and prospectively in a pilot implementation trial. Patients in cancer genetics and in PC clinics were invited to participate via email or a card. Informed consent was completed online. RESULTS: INHERET aimed to integrate patient data by algorithms based on professional and best practice guidelines to elicit succinct, actionable recommendations that providers can use without affecting clinic workflow or encounter length. INHERET requires a 4th-grade reading level, has simple navigation, and produces data lists and pedigree graphs. Prospective implementation testing revealed understandability of 90% to 100%, ease of use of 85%, and completion rates of 85% to 100%. Physicians using INHERET reported no added time to their encounters when patients were identified for counseling. In a specialty genetics clinic, INHERET's data were input, on average, within 72 hours compared with 4 to 6 weeks through standard care, and the queue for scheduling patients decreased from 400 to fewer than 15 in <6 months. CONCLUSIONS: INHERET was found to be accessible for all education and age levels, except patients aged >70 years, who encountered more technical difficulties. INHERET aided providers in conveying high-risk status to patients and eliciting appropriate referrals, and, in a specialty clinic, it produced improved workflows and shortened queues.

Rational design of an essential diagnostics network to support Universal Health Coverage: a modeling analysis.

BACKGROUND: Diagnostic investigations, including pathology and laboratory medicine (PALM) and radiology, have been largely absent from international strategies such as the Sustainable Development Goals. Further, there is little international guidance on which health system tiers different diagnostics should be placed, a critical step in developing a country-level diagnostics network. We describe a modeling strategy to produce tier-specific diagnostic recommendations based on disease burden, current treatment pathways, and existing infrastructure in a country. METHODS: The relational model assumes that diagnostics should be available at the lowest tier where patients might receive medical management. Using Ghana as an exemplar, the 20 diseases forecasted by 2030 and 2040 to cause the greatest burden in low- and middle-income countries were mapped to three generalized tiers in the Ghanaian health system (Primary, Secondary, and Tertiary care) for three levels of each disease (triage, uncomplicated, and complicated). The lowest tier at which a diagnostic could potentially be placed was restricted by existing infrastructure, though placement still required there be a medical justification for the diagnostic at that tier. RESULTS: The model recommended 111 unique diagnostic investigations with 17 at Primary tier, an additional 45 at Secondary tier and a further 49 at Tertiary tier. Estimated capital costs were $8,330 at Primary tier and between $571,000 to $777,000 at Secondary tier. Twenty-eight different laboratory tests were recommended as send-outs from Primary to Secondary tier, and twelve as send-outs to Tertiary tier. CONCLUSIONS: This model provides a transparent framework within which countries can customize diagnostic planning to local disease priorities, health system patient treatment pathways, and infrastructural limitations to best support Universal Health Coverage.

Essential Diagnostics for the Use of World Health Organization Essential Medicines.

BACKGROUND: There are numerous barriers to achieving high-quality laboratory diagnostic testing in resource-limited countries. These include inconsistent supply chains, variable quality of diagnostic devices, lack of human and financial resources, the ever-growing list of available tests, and a historical reliance on syndromic treatment algorithms. A list of essential diagnostics based on an accepted standard like the WHO Essential Medicines List (EML) could coordinate stakeholders in the strengthening of laboratory capacity globally. METHODS: To aid in the creation of an essential diagnostics list (EDL), we identified laboratory test indications from expert databases for the safe and effective use of WHO EML medicines. In all, 446 EML medicines were included in the study. We identified 279 conditions targeted by these medicines, spanning communicable and noncommunicable diseases (e.g., HIV, diabetes mellitus). RESULTS: We found 325 unique diagnostic tests, across 2717 indications, associated with the identified conditions or their associated medicines. The indications were divided into 10 categories: toxicity (865), diagnosis (591), monitoring (379), dosing/safety (325), complications (217), pathophysiology (154), differential diagnosis (97), comorbidities (53), drug-susceptibility testing (22), and companion diagnostic testing (14). We also created a sublist of 74 higher-priority tests to help define the core of the EDL. CONCLUSIONS: An EDL such as we describe here could align the global health community to solve the problems impeding equitable access to high-quality diagnostic testing in support of the global health agenda.

Clostridium difficile PCR Cycle Threshold Predicts Free Toxin.

There is no stand-alone Clostridium difficile diagnostic that can sensitively and rapidly detect fecal free toxins. We investigated the performance of the C. difficile PCR cycle threshold (CT ) for predicting free toxin status. Consecutive stool samples (n = 312) positive for toxigenic C. difficile by the GeneXpert C. difficile/Epi tcdB PCR assay were tested with the rapid membrane C. Diff Quik Chek Complete immunoassay (RMEIA). RMEIA toxin-negative samples were tested with the cell cytotoxicity neutralization assay (CCNA) and tgcBIOMICS enzyme-linked immunosorbent assay (ELISA). Using RMEIA alone or in combination with CCNA and/or ELISA as the reference method, the accuracy of CT was measured at different CT cutoffs. Using RMEIA as the reference method, a CT cutoff of 26.35 detected toxin-positive samples with a sensitivity, specificity, positive predictive value, and negative predictive value of 96.0% (95% confidence interval [CI], 90.2% to 98.9%), 65.9% (95% CI, 59.0% to 72.2%), 57.4% (95% CI, 52.7% to 62%), and 97.1% (95% CI, 92.8% to 98.9), respectively. Inclusion of CCNA in the reference method improved CT specificity to 78.0% (95% CI, 70.7% to 84.2%). Intercartridge lot CT variability measured as the average coefficient of variation was 2.8% (95% CI, 1.2% to 3.2%). Standardizing the input stool volume did not improve CT toxin specificity. The median CT values were not significantly different between stool samples with Bristol scores of 5, 6, and 7, between pediatric and adult samples, or between presumptive 027 and non-027 strains. In addition to sensitively detecting toxigenic C. difficile in stool, on-demand PCR may also be used to accurately predict toxin-negative stool samples, thus providing additional results in PCR-positive stool samples to guide therapy.

Real-Time Electronic Tracking of Diarrheal Episodes and Laxative Therapy Enables Verification of Clostridium difficile Clinical Testing Criteria and Reduction of Clostridium difficile Infection Rates.

Health care-onset health care facility-associated Clostridium difficile infection (HO-CDI) is overdiagnosed for several reasons, including the high prevalence of C. difficile colonization and the inability of hospitals to limit testing to patients with clinically significant diarrhea. We conducted a quasiexperimental study from 22 June 2015 to 30 June 2016 on consecutive inpatients with C. difficile test orders at an academic hospital. Real-time electronic patient data tracking was used by the laboratory to enforce testing criteria (defined as the presence of diarrhea [≥3 unformed stools in 24 h] and absence of laxative intake in the prior 48 h). Outcome measures included C. difficile test utilization, HO-CDI incidence, oral vancomycin utilization, and clinical complications. During the intervention, 7.1% (164) and 9.1% (211) of 2,321 C. difficile test orders were canceled due to absence of diarrhea and receipt of laxative therapy, respectively. C. difficile test utilization decreased upon implementation from an average of 208.8 tests to 143.0 tests per 10,000 patient-days (P < 0.001). HO-CDI incidence rate decreased from an average of 13.0 cases to 9.7 cases per 10,000 patient-days (P = 0.008). Oral vancomycin days of therapy decreased from an average of 13.8 days to 9.4 days per 1,000 patient-days (P = 0.009). Clinical complication rates were not significantly different in patients with 375 canceled orders compared with 869 episodes with diarrhea but negative C. difficile results. Real-time electronic clinical data tracking is an effective tool for verification of C. difficile clinical testing criteria and safe reduction of inflated HO-CDI rates.

Postmarket Surveillance of Point-of-Care Glucose Meters through Analysis of Electronic Medical Records.

BACKGROUND: The electronic medical record (EMR) holds a promising source of data for active postmarket surveillance of diagnostic accuracy, particularly for point-of-care (POC) devices. Through a comparison with prospective bedside and laboratory accuracy studies, we demonstrate the validity of active surveillance via an EMR data mining method [Data Mining EMRs to Evaluate Coincident Testing (DETECT)], comparing POC glucose results to near-in-time central laboratory glucose results. METHODS: The Roche ACCU-CHEK Inform II(®) POC glucose meter was evaluated in a laboratory validation study (n = 73), a prospective bedside intensive care unit (ICU) study (n = 124), and with DETECT (n = 852-27 503). For DETECT, the EMR was queried for POC and central laboratory glucose results with filtering based on of bedside collection timestamps, central laboratory time delays, patient location, time period, absence of repeat testing, and presence of peripheral lines. RESULTS: DETECT and the bedside ICU study produced similar estimates of average bias (4.5 vs 5.0 mg/dL) and relative random error (6.3% vs 5.6%), with overlapping CIs. For glucose <100 mg/dL, the laboratory validation study estimated a lower relative random error of 3.6%. POC average bias correlated with central laboratory turnaround times, consistent with 4.8 mg · dL(-1) · h(-1) glycolysis. After glycolysis adjustment, average bias was estimated by the bedside ICU study at -0.4 mg/dL (CI, -1.6 to 0.9) and DETECT at -0.7 (CI, -1.3 to 0.2), and percentage POC results occurring outside Clinical Laboratory Standards Institute quality goals were 2.4% and 4.8%, respectively. CONCLUSIONS: This study validates DETECT for estimating POC glucose meter accuracy compared with a prospective bedside ICU study and establishes it as a reliable postmarket surveillance methodology.

Challenges of measuring monoclonal proteins in serum.

The measurement of monoclonal protein (M-protein) is vital for stratifying risk and following individuals with a variety of monoclonal gammopathies. Direct measurement of the M-protein spike by electrophoresis and immunochemical measurements of specific isotypes or free light chains pairs has provided useful information about the quantity of M-protein. Nonetheless, both traditional electrophoresis and immunochemical methods give poor quantification with M-proteins smaller than 10 g/L (1 g/dL) when in the presence of polyclonal immunoglobulins that co-migrate with the M-protein. In addition, measurements by electrophoresis of M-proteins migrating in the ß- and α-regions are contaminated by normal serum proteins in those regions. The most precise electrophoretic method to date for quantification involves exclusion of the polyclonal immunoglobulins by using the tangent skimming method on electropherograms, which provides a 10-fold improvement in precision. So far, however, tangent measurements are limited to γ migrating M-proteins. Another way to improve M-protein measurements is the use of capillary electrophoresis (CE). With CE, one can employ immunosubtraction to select a region of interest in the ß region thereby excluding much of the normal proteins from the M-protein measurement. Recent development of an immunochemical method distinguishing heavy/light chain pairs (separately measuring IgGK and IgGL, IgAK and IgAL, and IgMK and IgML) provides measurements that could exclude polyclonal contaminants of the same heavy chain with the uninvolved light chain type. Yet, even heavy/light results contain an immeasurable quantity of polyclonal heavy/light chains of the involved isotype. Finally, use of liquid chromatography-tandem mass spectrometry (LC-MS/MS) looms on the horizon as a means to provide more consistent and sensitive measurements of M-proteins.

An unsupervised learning method to identify reference intervals from a clinical database.

Reference intervals are critical for the interpretation of laboratory results. The development of reference intervals using traditional methods is time consuming and costly. An alternative approach, known as an a posteriori method, requires an expert to enumerate diagnoses and procedures that can affect the measurement of interest. We develop a method, LIMIT, to use laboratory test results from a clinical database to identify ICD9 codes that are associated with extreme laboratory results, thus automating the a posteriori method. LIMIT was developed using sodium serum levels, and validated using potassium serum levels, both tests for which harmonized reference intervals already exist. To test LIMIT, reference intervals for total hemoglobin in whole blood were learned, and were compared with the hemoglobin reference intervals found using an existing a posteriori approach. In addition, prescription of iron supplements were used to identify individuals whose hemoglobin levels were low enough for a clinician to choose to take action. This prescription data indicating clinical action was then used to estimate the validity of the hemoglobin reference interval sets. Results show that LIMIT produces usable reference intervals for sodium, potassium and hemoglobin laboratory tests. The hemoglobin intervals produced using the data driven approaches consistently had higher positive predictive value and specificity in predicting an iron supplement prescription than the existing intervals. LIMIT represents a fast and inexpensive solution for calculating reference intervals, and shows that it is possible to use laboratory results and coded diagnoses to learn laboratory test reference intervals from clinical data warehouses.

Economic evaluation of laboratory testing strategies for hospital-associated Clostridium difficile infection.

Clostridium difficile infection (CDI) is the most common cause of infectious diarrhea in health care settings, and for patients presumed to have CDI, their isolation while awaiting laboratory results is costly. Newer rapid tests for CDI may reduce this burden, but the economic consequences of different testing algorithms remain unexplored. We used decision analysis from the hospital perspective to compare multiple CDI testing algorithms for adult inpatients with suspected CDI, assuming patient management according to laboratory results. CDI testing strategies included combinations of on-demand PCR (odPCR), batch PCR, lateral-flow diagnostics, plate-reader enzyme immunoassay, and direct tissue culture cytotoxicity. In the reference scenario, algorithms incorporating rapid testing were cost-effective relative to nonrapid algorithms. For every 10,000 symptomatic adults, relative to a strategy of treating nobody, lateral-flow glutamate dehydrogenase (GDH)/odPCR generated 831 true-positive results and cost $1,600 per additional true-positive case treated. Stand-alone odPCR was more effective and more expensive, identifying 174 additional true-positive cases at $6,900 per additional case treated. All other testing strategies were dominated by (i.e., more costly and less effective than) stand-alone odPCR or odPCR preceded by lateral-flow screening. A cost-benefit analysis (including estimated costs of missed cases) favored stand-alone odPCR in most settings but favored odPCR preceded by lateral-flow testing if a missed CDI case resulted in less than $5,000 of extended hospital stay costs and <2 transmissions, if lateral-flow GDH diagnostic sensitivity was >93%, or if the symptomatic carrier proportion among the toxigenic culture-positive cases was >80%. These results can aid guideline developers and laboratory directors who are considering rapid testing algorithms for diagnosing CDI.

A Generalizable Decision-Making Framework for Selecting Onsite versus Send-out Clinical Laboratory Testing.

BACKGROUND: Laboratory networks provide services through onsite testing or through specimen transport to higher-tier laboratories. This decision is based on the interplay of testing characteristics, treatment characteristics, and epidemiological characteristics. OBJECTIVES: Our objective was to develop a generalizable model using the threshold approach to medical decision making to inform test placement decisions. METHODS: We developed a decision model to compare the incremental utility of onsite versus send-out testing for clinical purposes. We then performed Monte Carlo simulations to identify the settings under which each strategy would be preferred. Tuberculosis was modeled as an exemplar. RESULTS: The most important determinants of the decision to test onsite versus send-out were the clinical utility lost due to send-out testing delays and the accuracy decrement with onsite testing. When the sensitivity decrements of onsite testing were minimal, onsite testing tended to be preferred when send-out delays reduced clinical utility by >20%. By contrast, when onsite testing incurred large reductions in sensitivity, onsite testing tended to be preferred when utility lost due to delays was >50%. The relative cost of onsite versus send-out testing affected these thresholds, particularly when testing costs were >10% of treatment costs. CONCLUSIONS: Decision makers can select onsite versus send-out testing in an evidence-based fashion using estimates of the percentage of clinical utility lost due to send-out delays and the relative accuracy of onsite versus send-out testing. This model is designed to be generalizable to a wide variety of use cases. HIGHLIGHTS: The design of laboratory networks, including the decision to place diagnostic instruments at the point-of-care or at higher tiers as accessed through specimen transport, can be informed using the threshold approach to medical decision making.The most important determinants of the decision to test onsite versus send-out were the clinical utility lost due to send-out testing delays and the accuracy decrement with onsite testing.The threshold approach to medical decision making can be used to compare point-of-care testing accuracy decrements with the lost utility of treatment due to send-out testing delays.The relative cost of onsite versus send-out testing affected these thresholds, particularly when testing costs were >10% of treatment costs.

Yellow fever in Ghana: Predicting emergence and ecology from historical outbreaks.

Understanding the epidemiology and ecology of yellow fever in endemic regions is critical for preventing future outbreaks. Ghana is a high-risk country for yellow fever. In this study we estimate the epidemiology, ecological cycles, and areas at risk for yellow fever in Ghana based on historical outbreaks. We identify 2371 cases and 887 deaths (case fatality rate 37.4%) from yellow fever reported in Ghana from 1910 to 2022. Since implementation of routine childhood vaccination in 1992, the estimated mean annual number of cases decreased by 81% and the geographic distribution of yellow fever cases also changed. While there have been multiple large historical outbreaks of yellow fever in Ghana from the urban cycle, recent outbreaks have originated among unvaccinated nomadic groups in rural areas with the sylvatic/savanna cycles. Using machine learning and an ecological niche modeling framework, we predict areas in Ghana that are similar to where prior yellow fever outbreaks have originated based on temperature, precipitation, landcover, elevation, and human population density. We find differences in predictions depending on the ecological cycles of outbreaks. Ultimately, these findings and methods could be used to inform further subnational risk assessments for yellow fever in Ghana and other high-risk countries.

Feasibility and Performance of Continuous Glucose Monitoring to Guide Computerized Insulin Infusion Therapy in Cardiovascular Intensive Care Unit.

BACKGROUND: We evaluated the feasibility of real-time continuous glucose monitoring (CGM) for titrating continuous intravenous insulin infusion (CII) to manage hyperglycemia in postoperative individuals in the cardiovascular intensive care unit and assessed their accuracy, nursing acceptance, and postoperative individual satisfaction. METHODS: Dexcom G6 CGM devices were applied to 59 postsurgical patients with hyperglycemia receiving CII. A hybrid approach combining CGM with periodic point-of-care blood glucose (POC-BG) tests with two phases (initial-ongoing) of validation was used to determine CGM accuracy. Mean and median absolute relative differences and Clarke Error Grid were plotted to evaluate the CGM accuracy. Surveys of nurses and patients on the use of CGMs experience were conducted and results were analyzed. RESULTS: In this cohort (mean age 64, 32% female, 32% with diabetes) with 864 paired POC-BG and CGM values analyzed, mean and median absolute relative difference between POC-BG and CGM values were 13.2% and 9.8%, respectively. 99.7% of paired CGM and POC-BG were in Zones A and B of the Clarke Error Grid. Responses from nurses reported CGMs being very or quite convenient (n = 28; 93%) and it was favored over POC-BG testing (n = 28; 93%). Majority of patients (n = 42; 93%) reported their care process using CGM as being good or very good. CONCLUSION: This pilot study demonstrates the feasibility, accuracy, and nursing convenience of adopting CGM via a hybrid approach for insulin titration in postoperative settings. These findings provide robust rationale for larger confirmatory studies to evaluate the benefit of CGM in postoperative care to improve workflow, enhance health outcomes, and cost-effectiveness.

Retrospective analysis of serum free light chain reference intervals and risk for monoclonal gammopathy suggests different limits than those in international guidelines.

OBJECTIVES: Recent reference interval studies of the serum free light chain (FLC) test using contemporary instruments display divergence with the diagnostic range generally adopted as the international standard. In this study, we perform a retrospective reference interval analysis with risk predictions for monoclonal gammopathy. METHODS: Retrospective laboratory and clinical data for 8,986 patients were included in the study. Reference intervals were generated against a set of inclusion/exclusion criteria for two time periods representing the use of different instruments. The presence of monoclonal gammopathy was established from diagnostic test interpretations and EHR diagnosis codes in the patient problem lists and medical history. RESULTS: The 95% FLC ratio reference intervals were 0.76-2.38 for SPAPLUS®, and 0.68-1.82 for Optilite® instruments. These intervals varied considerably from the current diagnostic range of 0.26-1.65 and mapped approximately to the FLC ratios beyond which risk of monoclonal gammopathy substantially increased. CONCLUSIONS: These findings corroborate recent reference interval studies and support recommendations for independent re-evaluation of intervals by institutions as well as an update of international guidelines.

Providing specimen transport through an online marketplace in the Northern region of Ghana.

Background: Integrated diagnostic networks, which are themselves dependent on robust specimen transport solutions, are fundamental to effective healthcare systems. Objective: This study aimed to pilot an online marketplace for the transport of specimens throughout a laboratory network in Ghana. Methods: Independent drivers were matched with health facilities that required specimen transport using a suite of mobile applications and web portals developed for this study. This marketplace was piloted with seven drivers, two laboratories, and five health facilities in Ghana's Northern region from March 2019 to October 2019. Results: During the pilot, 182 deliveries were completed for 691 patients, including 4118 laboratory tests for antenatal care, disease surveillance, and clinical testing. Testing included 34 tests for communicable and non-communicable diseases. All but two specimens (laboratory cancellations) were successfully delivered and tested. The median time from request to encrypted emailing of results was 19.7 h, while that for a drop-off request was 0.9 h. In the midwife registry, the median time from patient visit to result recording was 1 day, compared to 4 days in the same months in 2018, and the number of mothers without documented testing decreased from 41 to 3. Similarly, the proportion of tuberculosis specimen deliveries from Buipe Polyclinic to Tamale Zonal Laboratory taking over 1 day fell from 62% at baseline to 3% during the pilot. Conclusion: An online marketplace successfully orchestrated the delivery of laboratory specimens under a variety of clinical circumstances, reducing overall turn-around time without diminution of the overall specimen delivery process. What this study adds: This study established the efficacy of an online marketplace to orchestrate timely and high-quality delivery of specimens within a laboratory network.

An assessment of the laboratory network in Ghana: A national-level ATLAS survey (2019-2020).

Background: Integrated health systems with strong laboratory networks are critical in improving public health. The current study assessed the laboratory network in Ghana and its functionality using the Assessment Tool for Laboratory Services (ATLAS). Intervention: A national-level laboratory network survey was conducted among stakeholders of the Ghanaian laboratory network in Accra. Face-to-face interviews were conducted from December 2019 to January 2020, with follow-up phone interviews between June and July 2020. Also, we reviewed supporting documents provided by stakeholders for supplementary information and transcribed these to identify themes. Where possible, we completed the Laboratory Network scorecard using data obtained from the ATLAS. Lessons learnt: The Laboratory Network (LABNET) scorecard assessment was a valuable addition to the ATLAS survey as it quantified the functionality of the laboratory network and its overall advancement toward achieving International Health Regulations (2005) and Global Health Security Agenda targets. Two significant challenges indicated by respondents were laboratory financing and delayed implementation of the Ghana National Health Laboratory Policy. Recommendations: Stakeholders recommended a review of the country's funding landscape, such as funding laboratory services from the country's internally generated funds. Also, they recommended laboratory policy implementation to ensure adequate laboratory workforce and standards.