The Danish Hospital-Acquired Infections Database (HAIBA) is an automated surveillance system using hospital administrative, microbiological, and antibiotic medication data.
To define and evaluate the case definition for hospital-acquired urinary tract infection (HA-UTI) and to describe surveillance data from 2010 to 2014.
The HA-UTI algorithm defined a laboratory-diagnosed UTI as a urine culture positive for no more than two micro-organisms with at least one at =10(4)cfu/mL, and a probable UTI as a negative urine culture and a relevant diagnosis code or antibiotic treatment. UTI was considered hospital-acquired if a urine sample was collected =48h after admission and
Monitoring of hospital-acquired infection (HAI) by automated compilation of registry data may address the disadvantages of laborious, costly and potentially subjective and often random sampling of data by manual surveillance.
To evaluate a system for automated monitoring of hospital-acquired urinary tract (HA-UTI) and bloodstream infections (HA-BSI) and to report incidence rates over a five-year period in a Danish hospital trust.
Based primarily on electronically available data relating to microbiology results and antibiotic prescriptions, the automated monitoring of HA-UTIs and HA-BSIs was validated against data from six previous point-prevalence surveys (PPS) from 2010 to 2013 and data from a manual assessment (HA-UTI only) of one department of internal medicine from January 2010. Incidence rates (infections per 1000 bed-days) from 2010 to 2014 were calculated.
Compared with the PPSs, the automated monitoring showed a sensitivity of 88% in detecting UTI in general, 78% in detecting HA-UTI, and 100% in detecting BSI in general. The monthly incidence rates varied between 4.14 and 6.61 per 1000 bed-days for HA-UTI and between 0.09 and 1.25 per 1000 bed-days for HA-BSI.
Replacing PPSs with automated monitoring of HAIs may provide better and more objective data and constitute a promising foundation for individual patient risk analyses and epidemiological studies. Automated monitoring may be universally applicable in hospitals with electronic databases comprising microbiological findings, admission data, and antibiotic prescriptions.
A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model.
Our objective was to evaluate automation-assisted screening, in comparison to the conventional method, in a routine population-based cervical cancer-screening programme. Our study is based on an individually randomised design involving approximately 160,000 invitees and 110,000 attendees every year. From 1999 to 2001, 471,297 women were invited to attend and 330,445 smears were screened (attendance rate 70.1%), of which 220,254 were tested conventionally and 110,191 were tested using the automation-assisted method. Cytologic Papanicolaou group II findings were reported slightly more often (RR = 1.04) in the automation-assisted method than in the conventional screening arm. There were 1,291 cases of histologically confirmed dysplasia or carcinoma (0.4% of the screened), one-third of which were severe dysplasia or a more severe finding (CIN3+). The detection rates of histologically verified findings were similar between the 2 screening arms. In Finland, the screening programme has been effective. As the detection rates, particularly of CIN3+, were similar between the screening arms, we will continue the automation-assisted method in the routine screening programme. Further follow-up for interval cancer incidence is required, however, to measure if the effect of screening is the same between the arms. A similar evaluation design is feasible to any other major or competing modification of the screening test or other element in the programme.
Risk assessment of future osteoporotic vertebral fractures is currently based mainly on risk factors, such as bone mineral density, age, prior fragility fractures, and smoking. It can be argued that an osteoporotic vertebral fracture is not exclusively an abrupt event but the result of a decaying process. To evaluate fracture risk, a shape-based classifier, identifying possible small prefracture deformities, may be constructed.
During a longitudinal case-control study, a large population of postmenopausal women, fracture free at baseline, were followed. The 22 women who sustained at least one lumbar fracture on follow-up represented the case group. The control group comprised 91 women who maintained skeletal integrity and matched the case group according to the standard osteoporosis risk factors. On radiographs, a radiologist and two technicians independently performed manual annotations of the vertebrae, and fracture prediction using shape features extracted from the baseline annotations was performed. This was implemented using posterior probabilities from a standard linear classifier.
The classifier tested on the study population quantified vertebral fracture risk, giving statistically significant results for the radiologist annotations (area under the curve, 0.71 ± 0.013; odds ratio, 4.9; 95% confidence interval, 2.94-8.05).
The shape-based classifier provided meaningful information for the prediction of vertebral fractures. The approach was tested on case and control groups matched for osteoporosis risk factors. Therefore, the method can be considered an additional biomarker, which combined with traditional risk factors can improve population selection (eg, in clinical trials), identifying patients with high fracture risk.