Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis.

Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

https://www.bmj.com/content/bmj/369/bmj.m1328.full.pdf

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Background Since December 2019, acute respiratory disease (ARD) due to 2019 novel coronavirus (2019-nCoV) emerged in Wuhan city and rapidly spread throughout China. We sought to delineate the clinical characteristics of these cases. Methods We extracted the data on 1,099 patients with laboratory-confirmed 2019-nCoV ARD from 552 hospitals in 31 provinces/provincial municipalities through January 29th, 2020. Results The median age was 47.0 years, and 41.90% were females. Only 1.18% of patients had a direct contact with wildlife, whereas 31.30% had been to Wuhan and 71.80% had contacted with people from Wuhan. Fever (87.9%) and cough (67.7%) were the most common symptoms. Diarrhea is uncommon. The median incubation period was 3.0 days (range, 0 to 24.0 days). On admission, ground-glass opacity was the typical radiological finding on chest computed tomography (50.00%). Significantly more severe cases were diagnosed by symptoms plus reverse-transcriptase polymerase-chain-reaction without abnormal radiological findings than non-severe cases (23.87% vs. 5.20%, P Conclusions The 2019-nCoV epidemic spreads rapidly by human-to-human transmission. Normal radiologic findings are present among some patients with 2019-nCoV infection. The disease severity (including oxygen saturation, respiratory rate, blood leukocyte/lymphocyte count and chest X-ray/CT manifestations) predict poor clinical outcomes.

https://medrxiv.org/content/10.1101/2020.02.06.20020974v1.full.pdf

Clinical characteristics of 2019 novel coronavirus infection in China

Purpose To predict the risk of developing severe pneumonia among mild novel coronavirus pneumonia (mNCP) patients on admission. Methods A retrospective cohort study was conducted at three hospitals in Shanghai and Wuhan from January 2020 to February 2020. Real-time polymerasechain-reaction assays were used to detect COVID-19. A total of 529 patients diagnosed with NCP were recruited from three hospitals and classified by four severity types during hospitalization following the standards of the Chinese Diagnosis and Treatment of Pneumonia Caused by New Coronavirus Infection (eighth version). Patients were excluded if admitted by ICU on admission (n=92, on a general ward while meeting the condition of severe or critical type on admission (n=25), or there was insufficient clinical information (n=64). In sum, 348 patients with mNCP were finally included, and 68 developed severe pneumonia. Results mNCP severity prognostic index values were calculated based on multivariate logistic regression: history of diabetes (OR 2.064, 95% CI 1.010-4.683; p=0.043), time from symptom onset to admission ≥7 days (OR 1.945, 95% CI 1.054-3.587; p=0.033), lymphocyte count ≤0.8 (OR 1.816, 95% CI 1.008-3.274; p=0.047), myoglobin ≥90 mg/L (OR 2.496, 95% CI 1.235-5.047; p=0.011), and D-dimer ≥0.5 mg/L (OR 2.740, 95% CI 1.395-5.380; p=0.003). This model showed a c-statistics of 0.747, with sensitivity and specificity 0.764 and 0.644, respectively, under cutoff of 165. Conclusion We designed a clinical predictive tool for risk of severe pneumonia among mNCP patients to provided guidance for medicines. Further studies are required for external validation.

/pdf/the-mncp-spi-score-predicting-risk-of-severe-covid-19-among-4qld0bevvu.pdf

The mncp-spi score predicting risk of severe covid-19 among mild-pneumonia patients on admission

Nowadays, the world has changed a lot and many cities had made remarkable leaps and bounds, whether it is the economy, the level of medical care or the quality of teaching and learning have all made great progress. But among the many developed areas, there are still other areas that have not been well improved, not only the economy has not been improved, but slowly withdrawn from the trend circle of this modern and fast developing era. Then nowadays new media communication is a good solution to this problem, not only can increase the number of tourists through video propaganda, but also through live with goods, video with goods and other ways to solve the problem of poor areas with a single sales channel and low attention, thus driving the local development and economic improvement.

/pdf/modern-new-media-drive-which-development-in-poor-areas-3p8hfw5y.pdf

Modern New Media Drive Which Development in Poor Areas

Objective Risk factors for the development of pneumonia among patients with diabetes mellitus are unclear. The aim of our study was to elucidate the potential risk factors and attempt to predict the probability of pneumonia based on the history of diabetes. Methods We performed a population-based, prospective multicenter cohort study of 1,043 adult patients with diabetes in China during 2017-2019. Demographic information, comorbidities, or laboratory examinations were collected. Results The study included 417 diabetic patients with pneumonia and 626 no-pneumonia-onset diabetic patients. The predictive risk factors were chosen on the basis of a multivariate logistic regression model to predict pneumonia among patients with diabetes including male sex [odds ratio (OR) = 1.72, 95% confidence interval (CI): 1.27-2.33, p < 0.001], age ≥ 75 years (OR = 2.31, 95% CI: 1.61-3.31, p < 0.001), body mass index < 25 (OR = 2.59, 95% CI: 1.92-3.50, p < 0.001), chronic obstructive pulmonary disease (OR = 6.58, 95% CI: 2.09-20.7, p = 0.001), hypertension (OR = 4.27, 95% CI: 3.12-5.85, p < 0.001), coronary heart disease (OR = 2.98, 95% CI: 1.61-5.52, p < 0.001), renal failure (OR = 1.82, 95% CI: 1.002-3.29, p = 0.049), cancer (OR = 3.57, 95% CI: 1.80-7.06, p < 0.001), use of insulin (OR = 2.28, 95% CI: 1.60-3.25, p < 0.001), and hemoglobin A1c ≥ 9% (OR = 2.70, 95% CI: 1.89-3.85, p < 0.001). A predictive nomogram was established. This model showed c-statistics of 0.811, and sensitivity and specificity were 0.717 and 0.780, respectively, under cut-off of 125 score. Conclusion We designed a clinically predictive tool for assessing the risk of pneumonia among adult patients with diabetes. This tool stratifies patients into relevant risk categories and may provide a basis for individually tailored intervention for the purpose of early prevention.

/pdf/a-new-prognostic-index-pdpi-for-the-risk-of-pneumonia-among-196by4f5j6.pdf

A New Prognostic Index PDPI for the Risk of Pneumonia Among Patients With Diabetes

Background Glucose management is of great significance. Infection and hyperglycemia are a vicious circle. This study was conducted to describe distribution and antimicrobial resistance of bacteria isolated from patients with normoglycemia, hyperglycemia, or diabetes on admission. Methods A retrospective study was conducted in a teaching hospital from January 2015 to March 2017. Bacteria were identified by the Vitek 2 automated system and antimicrobial susceptibility determined. Results A total of 1,163 patients were included: 582 with normoglycemia, 292 with hyperglycemia and 289 with diabetes. Enterobacter, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterococcus faecium were the main species isolated from these patients, with 1,616 unduplicated isolates from sputum samples. Patients with hyperglycemia were more prone to carry more than one species, and the rate of multidrug-resistant K. pneumoniae and methicillin-resistant S. aureus was higher in this group. K. pneumoniae from hyperglycemia patients demonstrated increased resistance to carbapenems, especially imipenem (p=0.002) and meropenem (p=0.003), than those isolated from patients with normoglycemia or diabetes. No significance was detected for K. pneumoniae, A. baumannii, or P. aeruginosa between nondiabetes and diabetes patients. In addition, hyperglycemia patients had a higher rate of ICU admission (p=0.035) and a lower survival rate (p<0.001). Conclusion Patients with hyperglycemia were more prone to carry bacteria, especially multidrug-resistant K. pneumoniae and methicillin-resistant S. aureus. Assessing glucose on admission is of great significance in predicting bacterial carriage and antimicrobial resistance.

/pdf/increased-antimicrobial-resistance-among-sputum-pathogens-2a63xbywxj.pdf

Increased Antimicrobial Resistance among Sputum Pathogens from Patients with Hyperglycemia

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ctm2.303

Identification of distinct metabolic characteristics of pneumonia in type 2 diabetes mellitus.

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