Modeling reductions in SARS-CoV-2 transmission and hospital burden achieved by prioritizing testing using a clinical prediction rule

Prompt identification of cases is critical for slowing the spread of COVID-19. However, many areas have faced diagnostic testing shortages, requiring difficult decisions to be made regarding who receives a test, without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. We used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive, and found that its application to prioritize testing increases the proportion of those testing positive in settings of limited testing capacity. To consider the implications of these gains in daily case detection on the population level, we incorporated testing using the CPR into a compartmentalized disease transmission model. We found that prioritized testing led to a delayed and lowered infection peak (i.e. 'flattens the curve'), with the greatest impact at lower values of the effective reproductive number (such as with concurrent social distancing measures), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden. In conclusion, we present a novel approach to evidence-based allocation of limited diagnostic capacity, to achieve public health goals for COVID-19.

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Using a clinical prediction rule to prioritize diagnostic testing leads to reduced transmission and hospital burden: A modeling example of early SARS-CoV-2

Clinical Infectious Diseases ◽

10.1093/cid/ciab177 ◽

2021 ◽

Author(s):

Jody R Reimer ◽

Sharia M Ahmed ◽

Benjamin Brintz ◽

Rashmee U Shah ◽

Lindsay T Keegan ◽

...

Keyword(s):

Diagnostic Testing ◽

Prediction Rule ◽

Population Level ◽

Clinical Prediction Rule ◽

Emerging Diseases ◽

Reproductive Number ◽

Clinical Prediction ◽

Health Records ◽

Diagnostic Capacity ◽

Limited Testing

Abstract Background Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. Methods Using early SARS-CoV-2 as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2. Results We found that applying this CPR (AUC: 0.69 (95% CI: 0.68 - 0.70)) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (i.e., “flattens the curve”), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden. Conclusion We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.

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Accuracy and usefulness of a clinical prediction rule and D-dimer testing in excluding deep vein thrombosis (DVT) in cancer patients

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.19522 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 19522-19522

Author(s):

M. Carrier ◽

A. Lee ◽

S. Bates ◽

P. S. Wells

Keyword(s):

Cancer Patients ◽

Diagnostic Testing ◽

Prediction Rule ◽

Clinical Prediction Rule ◽

Clinical Prediction ◽

Probability Score ◽

Vein Thrombosis ◽

Clinical Probability ◽

Low Probability ◽

D Dimer

19522 Background: Cancer patients frequently present with thrombotic complications and rapid, accurate diagnostic testing would reduce morbidity and mortality. Although the combination of a low clinical probability using clinical prediction rules (e.g. Well’s Score) and a negative D-dimer result have proven to be safe and reliable in ruling out DVT in the general population, the accuracy of such a strategy is less certain in cancer patients. Because cancer patients often have alternative reasons for leg swelling and pain, and because malignancy and chemotherapy can render the D-dimer test positive in the absence of DVT, we hypothesize that the Well’s Score and D-dimer testing are potentially less accurate and less useful in excluding DVT in patients with active cancer. Methods: We performed a retrospective analysis of 2 prospective studies to compare the diagnostic test characteristics of the Well’s Score and D-dimer testing between patients with and without cancer presenting with suspected DVT. Results: A total of 1630 patients were studied; 107 had cancer. DVT was confirmed in 39.3% of patients with and 13.7% of patients without cancer. In both patient groups, the proportions of patients with DVT were significantly different among the high-, moderate- and low-probability groups according to the Well’s score (P<0.001). However, significantly fewer cancer patients (19.6%) had a low-probability score compared to patients without cancer (47.5%) (P<0.001). Similarly, 36.4% of cancer vs. 60.4% of noncancer patients had a negative D-dimer result (P<0.001). In cancer patients, a low probability score alone had a sensitivity of 95.2% (95%CI 82.6%-99.2%) and a specificity of 29.2% (95% CI 18.9%-42.0%). In combination with D-dimer testing, the sensitivity improved to 100% (95%CI 31.0%-100%) but the specificity was reduced to 26.4% (95%CI 13.5%-44.7%). In contrast, the specificity in patients without cancer was preserved at 53.9% (95%CI 50.4%-57.3%). Conclusion: DVT can be ruled out in cancer patients with a low clinical probability of DVT and a negative D-dimer result. However, the low specificity of these tests excludes very few patients and thereby limits their clinical usefulness. No significant financial relationships to disclose.

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Faculty Opinions recommendation of Derivation and validation of a clinical prediction rule for uncomplicated ureteral stone--the STONE score: retrospective and prospective observational cohort studies.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718326320.793493330 ◽

2014 ◽

Author(s):

M Hammad Ather

Keyword(s):

Cohort Studies ◽

Ureteral Stone ◽

Prediction Rule ◽

Clinical Prediction Rule ◽

Clinical Prediction ◽

Observational Cohort

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Mask-Ematics: Modeling the Effects of Masks in COVID-19 Transmission in High-Risk Environments

Epidemiologia ◽

10.3390/epidemiologia2020016 ◽

2021 ◽

Vol 2 (2) ◽

pp. 207-226

Author(s):

Anthony Morciglio ◽

Bin Zhang ◽

Gerardo Chowell ◽

James M. Hyman ◽

Yi Jiang

Keyword(s):

Public Health ◽

High Risk ◽

Disease Transmission ◽

Large Fraction ◽

Critical Mass ◽

Transmission Model ◽

Reproductive Number ◽

High Quality ◽

Moderate Quality ◽

Epidemic Size

The COVID-19 pandemic has placed an unprecedented burden on public health and strained the worldwide economy. The rapid spread of COVID-19 has been predominantly driven by aerosol transmission, and scientific research supports the use of face masks to reduce transmission. However, a systematic and quantitative understanding of how face masks reduce disease transmission is still lacking. We used epidemic data from the Diamond Princess cruise ship to calibrate a transmission model in a high-risk setting and derive the reproductive number for the model. We explain how the terms in the reproductive number reflect the contributions of the different infectious states to the spread of the infection. We used that model to compare the infection spread within a homogeneously mixed population for different types of masks, the timing of mask policy, and compliance of wearing masks. Our results suggest substantial reductions in epidemic size and mortality rate provided by at least 75% of people wearing masks (robust for different mask types). We also evaluated the timing of the mask implementation. We illustrate how ample compliance with moderate-quality masks at the start of an epidemic attained similar mortality reductions to less compliance and the use of high-quality masks after the epidemic took off. We observed that a critical mass of 84% of the population wearing masks can completely stop the spread of the disease. These results highlight the significance of a large fraction of the population needing to wear face masks to effectively reduce the spread of the epidemic. The simulations show that early implementation of mask policy using moderate-quality masks is more effective than a later implementation with high-quality masks. These findings may inform public health mask-use policies for an infectious respiratory disease outbreak (such as one of COVID-19) in high-risk settings.

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Inter‐rater reliability and prospective validation of a clinical prediction rule for SARS‐CoV‐2 infection

Academic Emergency Medicine ◽

10.1111/acem.14309 ◽

2021 ◽

Author(s):

Adam E. Nevel ◽

Jeffrey A. Kline

Keyword(s):

Prediction Rule ◽

Clinical Prediction Rule ◽

Clinical Prediction ◽

Rater Reliability

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Development and validation of a clinical prediction rule for development of diabetic foot ulceration: an analysis of data from five cohort studies

BMJ Open Diabetes Research & Care ◽

10.1136/bmjdrc-2021-002150 ◽

2021 ◽

Vol 9 (1) ◽

pp. e002150

Author(s):

Francesca M Chappell ◽

Fay Crawford ◽

Margaret Horne ◽

Graham P Leese ◽

Angela Martin ◽

...

Keyword(s):

Cohort Studies ◽

Secondary Care ◽

Prediction Rule ◽

Clinical Prediction Rule ◽

Initial Assessment ◽

Validation Dataset ◽

Benefit Analysis ◽

Clinical Prediction ◽

Net Benefit ◽

Foot Ulceration

IntroductionThe aim of the study was to develop and validate a clinical prediction rule (CPR) for foot ulceration in people with diabetes.Research design and methodsDevelopment of a CPR using individual participant data from four international cohort studies identified by systematic review, with validation in a fifth study. Development cohorts were from primary and secondary care foot clinics in Europe and the USA (n=8255, adults over 18 years old, with diabetes, ulcer free at recruitment). Using data from monofilament testing, presence/absence of pulses, and participant history of previous ulcer and/or amputation, we developed a simple CPR to predict who will develop a foot ulcer within 2 years of initial assessment and validated it in a fifth study (n=3324). The CPR’s performance was assessed with C-statistics, calibration slopes, calibration-in-the-large, and a net benefit analysis.ResultsCPR scores of 0, 1, 2, 3, and 4 had a risk of ulcer within 2 years of 2.4% (95% CI 1.5% to 3.9%), 6.0% (95% CI 3.5% to 9.5%), 14.0% (95% CI 8.5% to 21.3%), 29.2% (95% CI 19.2% to 41.0%), and 51.1% (95% CI 37.9% to 64.1%), respectively. In the validation dataset, calibration-in-the-large was −0.374 (95% CI −0.561 to −0.187) and calibration slope 1.139 (95% CI 0.994 to 1.283). The C-statistic was 0.829 (95% CI 0.790 to 0.868). The net benefit analysis suggested that people with a CPR score of 1 or more (risk of ulceration 6.0% or more) should be referred for treatment.ConclusionThe clinical prediction rule is simple, using routinely obtained data, and could help prevent foot ulcers by redirecting care to patients with scores of 1 or above. It has been validated in a community setting, and requires further validation in secondary care settings.

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Developing a clinical prediction rule for repeated consultations with functional somatic symptoms in primary care: a cohort study

BMJ Open ◽

10.1136/bmjopen-2020-040730 ◽

2021 ◽

Vol 11 (1) ◽

pp. e040730

Author(s):

Gea A Holtman ◽

Huibert Burger ◽

Robert A Verheij ◽

Hans Wouters ◽

Marjolein Y Berger ◽

...

Keyword(s):

Primary Care ◽

Cohort Study ◽

Somatic Symptoms ◽

Prediction Rule ◽

Clinical Prediction Rule ◽

Model Discrimination ◽

Clinical Prediction ◽

Internal Validation ◽

Predictive Values ◽

Healthy Activity

ObjectivesPatients who present in primary care with chronic functional somatic symptoms (FSS) have reduced quality of life and increased health care costs. Recognising these early is a challenge. The aim is to develop and internally validate a clinical prediction rule for repeated consultations with FSS.Design and settingRecords from the longitudinal population-based (‘Lifelines’) cohort study were linked to electronic health records from general practitioners (GPs).ParticipantsWe included patients consulting a GP with FSS within 1 year after baseline assessment in the Lifelines cohort.Outcome measuresThe outcome is repeated consultations with FSS, defined as ≥3 extra consultations for FSS within 1 year after the first consultation. Multivariable logistic regression, with bootstrapping for internal validation, was used to develop a risk prediction model from 14 literature-based predictors. Model discrimination, calibration and diagnostic accuracy were assessed.Results18 810 participants were identified by database linkage, of whom 2650 consulted a GP with FSS and 297 (11%) had ≥3 extra consultations. In the final multivariable model, older age, female sex, lack of healthy activity, presence of generalised anxiety disorder and higher number of GP consultations in the last year predicted repeated consultations. Discrimination after internal validation was 0.64 with a calibration slope of 0.95. The positive predictive value of patients with high scores on the model was 0.37 (0.29–0.47).ConclusionsSeveral theoretically suggested predisposing and precipitating predictors, including neuroticism and stressful life events, surprisingly failed to contribute to our final model. Moreover, this model mostly included general predictors of increased risk of repeated consultations among patients with FSS. The model discrimination and positive predictive values were insufficient and preclude clinical implementation.

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