Coronavirus disease 2019 (COVID-19) is sweeping the globe. Despite multiple case-series, actionable knowledge to tailor decision-making proactively is missing.
Can a statistical model accurately ...predict infection with COVID-19?
We developed a prospective registry of all patients tested for COVID-19 in Cleveland Clinic to create individualized risk prediction models. We focus here on the likelihood of a positive nasal or oropharyngeal COVID-19 test. A least absolute shrinkage and selection operator logistic regression algorithm was constructed that removed variables that were not contributing to the model’s cross-validated concordance index. After external validation in a temporally and geographically distinct cohort, the statistical prediction model was illustrated as a nomogram and deployed in an online risk calculator.
In the development cohort, 11,672 patients fulfilled study criteria, including 818 patients (7.0%) who tested positive for COVID-19; in the validation cohort, 2295 patients fulfilled criteria, including 290 patients who tested positive for COVID-19. Male, African American, older patients, and those with known COVID-19 exposure were at higher risk of being positive for COVID-19. Risk was reduced in those who had pneumococcal polysaccharide or influenza vaccine or who were on melatonin, paroxetine, or carvedilol. Our model had favorable discrimination (c-statistic = 0.863 in the development cohort and 0.840 in the validation cohort) and calibration. We present sensitivity, specificity, negative predictive value, and positive predictive value at different prediction cutoff points. The calculator is freely available at https://riskcalc.org/COVID19.
Prediction of a COVID-19 positive test is possible and could help direct health-care resources. We demonstrate relevance of age, race, sex, and socioeconomic characteristics in COVID-19 susceptibility and suggest a potential modifying role of certain common vaccinations and drugs that have been identified in drug-repurposing studies.
Abstract Background There is little information about risk acceptance of multiple sclerosis (MS) patients to various MS therapies. Objective To determine MS patients׳ tolerance to risky therapies and ...identify associated characteristics. Methods MS patients from the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry׳s online cohort were invited to complete questionnaires on decision making and risk tolerance (RT) to two therapeutic scenarios: a theoretical cure for MS CureMS, with permanent reversal of all MS symptoms but a risk of immediate painless death; and natalizumab NAT, a real-life scenario with benefits and risks as defined by Phase III trial results. Results The median RT for both scenarios was 1:10,000; 15–23% of respondents were not willing to take any risk for their MS therapy. Participants with greater disability or not taking any MS therapy showed a greater RT, while females and those caring for dependents had a lower RT. Females and older age were predictors of lower RT, while increasing disability and greater blunting attitude with respect to information seeking behavior were predictors of higher RT. Conclusion MS patients displayed a wide range of RT for MS therapies. Our study identified gender, age, disability and information seeking behavior to be associated with RT.
OBJECTIVE
To determine whether p53 is an independent biomarker of prostate cancer outcome against currently used biomarkers in a cohort of conservatively treated prostate cancers with long‐term ...follow‐up available.
PATIENTS AND METHODS
We examined p53 expression by immunohistochemistry in a cohort of 705 patients with clinically localized prostate cancer, who were treated conservatively. Patients were selected through UK Cancer Registries. End‐points included prostate cancer death and overall death rates. Standard biological variables, including diagnostic serum PSA, contemporary Gleason scoring, clinical staging and cancer extent were available. p53 expression was measured semi‐quantitatively on microscopic examination and compared with current clinical biomarkers.
RESULTS
p53 over expression was a significant predictor of cause‐specific survival (hazard ratio HR 2.95, 95% CI 2.05–4.25, P < 0.001) and overall survival (HR 2.37, 95% CI 1.84–3.05, P < 0.001). In multivariate analysis including competing biological variables p53 expression was still significantly linked to prostate cancer survival (HR 1.51, 95% CI 1.04–2.19, P = 0.03) and overall survival (HR 1.57, 95% CI 1.21–2.05, P = 0.001).
CONCLUSIONS
We conclude that p53 may have a role in the future assessment of newly diagnosed prostate cancer, as it significantly adds to the current prognostic model.
OBJECTIVESThis study aimed to develop and internally validate a nomogram that facilitates decision making between patient and physician by predicting a woman’s individual probability of developing ...urinary (UI) or fecal incontinence (FI) after her first delivery.
METHODSThis study used Childbirth and Pelvic Symptoms Study data, which estimated the prevalence of postpartum UI and FI in primiparous women after vaginal or cesarean delivery. Two models were developed using antepartum variables, and 2 models were developed using antepartum plus labor and delivery variables. Urinary incontinence was defined by a response of leaking urine “sometimes” or “often” using the Medical, Epidemiological, and Social Aspects of Aging Questionnaire. Fecal incontinence was defined as any involuntary leakage of mucus, liquid, or solid stool using the Fecal Incontinence Severity Index. Logistic regression models allowing nonlinear effects were used and displayed as nomograms. Overall performance was assessed using the Brier score (zero equals perfect model) and concordance index (c-statistic).
RESULTSA total of 921 women enrolled in the Childbirth and Pelvic Symptoms Study, and 759 (82%) were interviewed by telephone 6 months postpartum. Two antepartum models were generated, which discriminated between women who will and will not develop UI (Brier score = 0.19, c-statistic = 0.69) and FI (Brier score = 0.10, c-statistic = 0.67) at 6 months and 2 models were generated (Brier score = 0.18, c-statistic= 0.68 and Brier score = 0.09, c-statistic = 0.68) for predicting UI and FI, respectively, for use after labor and delivery.
CONCLUSIONSThese models yielded 4 nomograms that are accurate for generating individualized prognostic estimates of postpartum UI and FI and may facilitate decision making in the prevention of incontinence.
To develop and validate a model for estimating the risk of lung cancer death in current and former smokers. The model is intended for use in analyzing a population of subjects who are undergoing lung ...cancer screening or receiving lung cancer chemoprevention, to determine whether the intervention has altered lung cancer mortality.
Model derivation was based on analyses of the placebo arm of the Carotene and Retinol Efficacy Trial. Model validation was based on analyses of three other longitudinal cohorts.
Observed and predicted number of deaths due to lung cancer.
In internal validation, the model was highly concordant and well calibrated. In external validation, the model predictions were similar to what was observed in all of the validation analyses. The predicted and observed deaths within 6 years were very similar when assessed in the Johns Hopkins Hospital trial of chest radiography and sputum cytology screening (176 predicted, 184 observed, p = 0.53), the Memorial Sloan-Kettering Cancer Center trial of chest radiography and sputum cytology screening (108 predicted, 114 observed, p = 0.57), and the National Health and Nutrition Evaluation Survey part I (24 predicted, 21 observed, p = 0.52).
The number of lung cancer deaths in a population of current or former smokers can be accurately predicted, making model-based evaluations of prevention and early detection interventions a useful adjunct to definitive randomized trials. We illustrate this potential use with a small example.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objective: The aim of this study was to predict seminal vesicle invasion (SVI) by developing a new nomogram based on clinical features including the status of cancer at the base of the prostate on ...systematic biopsy.
Methods: We studied the 466 patients with T1–3N0M0 prostate cancer who were treated with radical prostatectomy at three institutions. Preoperative clinical variables were correlated with the presence or absence of SVI with an area under the curve (AUC) of receiver–operator characteristics analysis. A nomogram was developed to predict SVI based on logistic regression analysis.
Results: A total of 81 patients (17%) had SVI. Cancer was present in a biopsy core from the base of the prostate in 209 patients, of whom 32.5% had SVI, compared with only 5% of the 257 patients without cancer at the base of the prostate (P < 0.005). On multivariate analysis, serum prostate‐specific antigen, biopsy Gleason score, clinical T stage, and presence or absence of cancer in a biopsy core at the base of the prostate were significant predictors of SVI (P < 0.005 for all). The AUC of a standard model including clinical stage, Gleason score, and prostate‐specific antigen was 0.83, which was significantly enhanced by including the presence of cancer at the base of the prostate (none, unilateral or bilateral lobes) (AUC 0.87, P= 0.023). Based on the logistic analysis, we developed the nomogram to predict SVI. The calibration plots appeared to be excellent.
Conclusion: The information of presence or absence of cancer at the base from prostate biopsy and the resulting nomogram allow an accurate prediction of SVI in patients undergoing radical prostatectomy for prostate cancer.
To assess changes in the clinical characteristics and treatment patterns of patients with newly diagnosed type 2 diabetes (T2D), the electronic health record system at Cleveland Clinic was used to ...create cross-sectional summaries of all patients with new-onset T2D in 2008 and 2013. Differences between the 2008 and 2013 data sets were assessed after adjusting for age, gender, race, and income. Approximately one-third of patients with newly diagnosed T2D in 2008 and 2013 had an A1C ≧8%, suggesting the continued presence of a delayed recognition of the disease. Patients with newly diagnosed T2D in 2008 were older than those in 2013. Hypertension, cardiovascular disease, and neuropathy were highly prevalent among patients diagnosed with T2D. The prevalence of neuropathy, cerebrovascular disease, and peripheral vascular disease increased from 2008 to 2013. Metformin was the most commonly prescribed antidiabetic medication. Sulfonylurea usage remained unchanged, while use of thiazolidinediones decreased considerably.
PDF
