Development and validation of a risk prediction model for hospital admission in COVID-19 patients presenting to primary care

BACKGROUND: There is a paucity of prognostic models for COVID-19 that are usable for in-office patient assessment in general practice (GP). OBJECTIVES: To develop and validate a risk prediction model for hospital admission with readily available predictors. METHODS: A retrospective cohort study linking GP records from 8 COVID-19 centres and 55 general practices in the Netherlands to hospital admission records. The development cohort spanned March to June 2020, the validation cohort March to June 2021. The primary outcome was hospital admission within 14 days. We used geographic leave-region-out cross-validation in the development cohort and temporal validation in the validation cohort. RESULTS: In the development cohort, 4,806 adult patients with COVID-19 consulted their GP (median age 56, 56% female); in the validation cohort 830 patients did (median age 56, 52% female). In the development and validation cohort respectively, 292 (6.1%) and 126 (15.2%) were admitted to the hospital within 14 days, respectively. A logistic regression model based on sex, smoking, symptoms, vital signs and comorbidities predicted hospital admission with a c-index of 0.84 (95% CI 0.83 to 0.86) at geographic cross-validation and 0.79 (95% CI 0.74 to 0.83) at temporal validation, and was reasonably well calibrated (intercept -0.08, 95% CI -0.98 to 0.52, slope 0.89, 95% CI 0.71 to 1.07 at geographic cross-validation and intercept 0.02, 95% CI -0.21 to 0.24, slope 0.82, 95% CI 0.64 to 1.00 at temporal validation). CONCLUSION: We derived a risk model using readily available variables at GP assessment to predict hospital admission for COVID-19. It performed accurately across regions and waves. Further validation on cohorts with acquired immunity and newer SARS-CoV-2 variants is recommended

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Association between general practice characteristics and use of out-of-hours GP cooperatives

Contains fulltext : 154585.pdf (publisher's version ) (Open Access)BACKGROUND: The use of out-of-hours healthcare services for non-urgent health problems is believed to be related to the organisation of daytime primary care but insight into underlying mechanisms is limited. Our objective was to examine the association between daytime general practice characteristics and the use of out-of-hours care GP cooperatives. METHODS: A cross-sectional observational study in 100 general practices in the Netherlands, connected to five GP cooperatives. In each GP cooperative, we took a purposeful sample of the 10 general practices with the highest use of out-of-hours care and the 10 practices with the lowest use. Practice and population characteristics were obtained by questionnaires, interviews, data extraction from patient registration systems and telephone accessibility measurements. To examine which aspects of practice organisation were associated with patients' use of out-of-hours care, we performed logistic regression analyses (low versus high out-of-hours care use), correcting for population characteristics. RESULTS: The mean out-of-hours care use in the high use group of general practices was 1.8 times higher than in the low use group. Day time primary care practices with more young children and foreigners in their patient populations and with a shorter distance to the GP cooperative had higher out-of-hours primary care use. In addition, longer telephone waiting times and lower personal availability for palliative patients in daily practice were associated with higher use of out-of-hours care. Moreover, out-of-hours care use was higher when practices performed more diagnostic tests and therapeutic procedures and had more assistant employment hours per 1000 patients. Several other aspects of practice management showed some non-significant trends: high utilising general practices tended to have longer waiting times for non-urgent appointments, lower availability of a telephone consulting hour, lower availability for consultations after 5 p.m., and less frequent holiday openings. CONCLUSIONS: Besides patient population characteristics, organisational characteristics of general practices are associated with lower use of out-of-hours care. Improving accessibility and availability of day time primary day care might be a potential effective way to improve the efficient use of out-of-hours care services

Gene expression profiling reveals two distinct subtypes of Merkel cell carcinoma

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Development and validation of a risk prediction model for hospital admission in COVID-19 patients presenting to primary care

BackgroundThere is a paucity of prognostic models for COVID-19 that are usable for in-office patient assessment in general practice (GP).ObjectivesTo develop and validate a risk prediction model for hospital admission with readily available predictors.MethodsA retrospective cohort study linking GP records from 8 COVID-19 centres and 55 general practices in the Netherlands to hospital admission records. The development cohort spanned March to June 2020, the validation cohort March to June 2021. The primary outcome was hospital admission within 14 days. We used geographic leave-region-out cross-validation in the development cohort and temporal validation in the validation cohort.ResultsIn the development cohort, 4,806 adult patients with COVID-19 consulted their GP (median age 56, 56% female); in the validation cohort 830 patients did (median age 56, 52% female). In the development and validation cohort respectively, 292 (6.1%) and 126 (15.2%) were admitted to the hospital within 14 days, respectively. A logistic regression model based on sex, smoking, symptoms, vital signs and comorbidities predicted hospital admission with a c-index of 0.84 (95% CI 0.83 to 0.86) at geographic cross-validation and 0.79 (95% CI 0.74 to 0.83) at temporal validation, and was reasonably well calibrated (intercept −0.08, 95% CI −0.98 to 0.52, slope 0.89, 95% CI 0.71 to 1.07 at geographic cross-validation and intercept 0.02, 95% CI −0.21 to 0.24, slope 0.82, 95% CI 0.64 to 1.00 at temporal validation).ConclusionWe derived a risk model using readily available variables at GP assessment to predict hospital admission for COVID-19. It performed accurately across regions and waves. Further validation on cohorts with acquired immunity and newer SARS-CoV-2 variants is recommended

Development and validation of a risk prediction model for hospital admission in COVID-19 patients presenting to primary care

There is a paucity of prognostic models for COVID-19 that are usable for in-office patient assessment in general practice (GP). To develop and validate a risk prediction model for hospital admission with readily available predictors. A retrospective cohort study linking GP records from 8 COVID-19 centres and 55 general practices in the Netherlands to hospital admission records. The development cohort spanned March to June 2020, the validation cohort March to June 2021. The primary outcome was hospital admission within 14 days. We used geographic leave-region-out cross-validation in the development cohort and temporal validation in the validation cohort. In the development cohort, 4,806 adult patients with COVID-19 consulted their GP (median age 56, 56% female); in the validation cohort 830 patients did (median age 56, 52% female). In the development and validation cohort respectively, 292 (6.1%) and 126 (15.2%) were admitted to the hospital within 14 days, respectively. A logistic regression model based on sex, smoking, symptoms, vital signs and comorbidities predicted hospital admission with a c-index of 0.84 (95% CI 0.83 to 0.86) at geographic cross-validation and 0.79 (95% CI 0.74 to 0.83) at temporal validation, and was reasonably well calibrated (intercept −0.08, 95% CI −0.98 to 0.52, slope 0.89, 95% CI 0.71 to 1.07 at geographic cross-validation and intercept 0.02, 95% CI −0.21 to 0.24, slope 0.82, 95% CI 0.64 to 1.00 at temporal validation). We derived a risk model using readily available variables at GP assessment to predict hospital admission for COVID-19. It performed accurately across regions and waves. Further validation on cohorts with acquired immunity and newer SARS-CoV-2 variants is recommended. A general practice prediction model based on signs and symptoms of COVID-19 patients reliably predicted hospitalisation. The model performed well in second-wave data with other dominant variants and changed testing and vaccination policies. In an emerging pandemic, GP data can be leveraged to develop prognostic models for decision support and to predict hospitalisation rates.</p