How good are we at managing acute kidney injury in hospital?

Introduction. Acute kidney injury (AKI) is a common clinical problem associated with adverse outcomes. This study identifies the incidence of AKI in two UK district general hospitals’ without on-site renal services and assesses AKI management and level of nephrologist input. Methods. The AKIN classification was used to identify 1020 AKI patients over 6 months. Data were collated on patient demographics, AKI management and referral to nephrology and intensive care services. Short/long-term renal outcomes were investigated. Patients were followed up for 14 months post-discharge. Results. Incidence of hospital-based AKI was 6.4%. Mean patient age was 73 years. There was 28.1% acute in-hospital mortality with a further 21.6% 14-month mortality. Only 8.3% of patients were referred to nephrology services for in-hospital review, and only 8.1% had outpatient nephrology follow-up. Compliance with the AKI National Confidential Enquiry into Patient Outcomes and Deaths (NCEPOD) recommendations was poor with 32.8% of patients having renal imaging and 15% of patients having acid–base status assessed. NCEPOD compliance improved with nephrology input. Patients referred to nephrology were likely to be younger with pre-existing CKD and severe AKI. 10.5% of AKI episodes were unrecognized. Forty percent of those with unrecognized AKI, (compared with 15% of recognized AKI) developed de novo or progression of pre-existing CKD. Conclusion. AKI in DGHs is mostly managed without nephrology input. There are significant shortcomings in AKI recognition and management in this setting. This is associated with poor mortality and long-term CKD. This study supports a need to improve the teaching and training of front-line medical staff in identifying AKI. Additionally, implementation of AKI e-alert systems may encourage early recognition and provide a prompt for renal referral

Predicting malignancy in thyroid nodules: feasibility of a predictive model integrating clinical, biochemical, and ultrasound characteristics

Background: Although the majority of thyroid nodules are benign the process of excluding malignancy is challenging and sometimes involves unnecessary surgical procedures. We explored the development of a predictive model for malignancy in thyroid nodules by integrating a combination of simple demographic, biochemical, and ultrasound characteristics. Methods: Retrospective case-record review. We reviewed records of patients with thyroid nodules referred to our institution from 2004 to 2011 (n = 536; female 84 %, mean age 51 years). All malignancy was proven histologically while benign disease was either confirmed histologically, or on cytology with minimum 36-month observation period. We focused on the following predictors: age, sex, smoking status, thyroid hormones (FT4 and TSH) and nodule characteristics on ultrasound. Variables were included in a multivariate logistic regression and bootstrap analyses were used to confirm results. Results: Independent predictors of malignancy in the fully adjusted model were TSH (OR 1.53, 95 % CI 1.10, 2.12, p = 0.01), male gender (OR 3.45, 95 % CI 1.33, 8.92, p = 0.01), microcalcifications (OR 6.32, 95 % CI 2.82, 14.1, p < 0.001), and irregular nodule margins (OR 5.45, 95 % CI 1.61, 18.6, p = 0.006) Bootstrap analyses strengthened these associations and a parsimonious analysis consisting of these variables and age-group demonstrated an area under the curve of 0.77. A predictive score was sensitive (86.9 %) at low scores and highly specific (94.87 %) at higher scores for distinguishing benign from malignant disease. Conclusions: A predictive model for malignancy using a combination of clinical, biochemical, and radiological characteristics may support clinicians in reducing unnecessary invasive procedures in patients with thyroid nodules

Epidemiology and outcomes in community-acquired versus hospital-acquired AKI

Background and objective Compared with AKI in hospitalized patients, little is known about patients sustaining AKI in the community and how this differs from AKI in hospital. This study compared epidemiology, risk factors, and short- and long-term outcomes for patients with community-acquired (CA) and hospital-acquired (HA) AKI. Design, setting, participants, & measurements A total of 15,976 patients admitted to two district general hospitals between July 11, 2011, and January 15, 2012 were studied. Through use of an electronic database and the AKI Network classification, 686 patients with CA-AKI and 334 patients with HA-AKI were identified. Patients were followed up for 14 months, and data were collated on short-term and long-term renal and patient outcomes. Results The incidence of CA-AKI among all hospital admissions was 4.3% compared with an incidence of 2.1% of HA-AKI, giving an overall AKI incidence of 6.4%. Patients with CA-AKI were younger than patients with HA-AKI. Risks for developing HA and CA-AKI were similar and included preexisting CKD, cardiac failure, ischemic heart disease, hypertension, diabetes, dementia, and cancer. Patients with CA-AKI were more likely to have stage 3 AKI and had shorter lengths of hospital stay than patients with HA-AKI. Those with CA-AKI had better (multivariate-adjusted) survival than patients with HA-AKI (hazard ratio, 1.8 [95% CI, 1.44–2.13; P<0.001] for HA-AKI group). Mortality for the CA-AKI group was 45%; 43.7% of these deaths were acute in-hospital deaths. Mortality for the HA-AKI group was 62.9%, with 68.1% of these deaths being acute in-hospital deaths. Renal referral rates were low across the cohorts (8.3%). Renal outcomes were similar in both CA-AKI and HA-AKI groups, with 39.4% and 33.6% of patients in both groups developing de novo CKD or progression of preexisting CKD within 14 months, respectively. Conclusion Patients with CA-AKI sustain more severe AKI than patients with HA-AKI. Despite having risk factors similar to those of patients with HA-AKI, patients with CA AKI have better short- and long-term outcomes

Recent trends in the incidence, geographical distribution, and survival from thyroid cancer in wales, 1985-2010

Background: Previous studies of thyroid cancer incidence in Wales have given varying results with suggestions of an excess of cases in geographic areas that were previously exposed to the radioactive fallout from the 1986 Chernobyl nuclear reactor incident. Our objective in this study was to provide an up-to-date comprehensive analysis of time trends in the incidence, geographical distribution, and survival from thyroid cancer in Wales. Methods: We identified thyroid cancer cases, registered from 1985 through 2010 in the Welsh Cancer Intelligence and Surveillance Unit (WCISU). Age standardized rates were determined from the European standard population. A Poisson regression model was fitted to assess temporal trends and rate ratios (RRs) and confidence intervals (CIs) were determined and compared across consecutive time periods: 1985-1997 and 1998-2010. Standardized incidence ratios were calculated for each of the 22 local authority areas. Relative survival and Kaplan-Meier curves were computed to analyze all cause and thyroid cancer-specific survival. Results: A total of 1747 thyroid cancer cases were registered from 1985 to 2010. Age standardized incidence rates were 2.8 and 1.2 per 100,000 population per year for females and males respectively. Incidence rates increased with time (RR 1.3 [CI 1.2-1.5], p65 years vs. 15-64 years) and patients with anaplastic thyroid cancer (p<0.001; anaplastic vs. other histological varieties). Conclusions: The incidence of thyroid cancer has increased in Wales, predominantly due to an increase in papillary cancers. The current geographical distribution of cases does not support a radiation effect in the region. Survival has remained poor for patients over the age of 65 years and those with anaplastic carcinoma

Additional file 1: Figure S1. of Predicting malignancy in thyroid nodules: feasibility of a predictive model integrating clinical, biochemical, and ultrasound characteristics

Area under the curve for model predicting malignancy. Area under ROC curve = 0.7667 (PDF 14 kb