Blood culture status and mortality among patients with suspected community-acquired bacteremia: a population-based cohort study

<p>Abstract</p> <p>Background</p> <p>Comparison of mortality among patients with positive and negative blood cultures may indicate the contribution of bacteremia to mortality. This study (1) compared mortality among patients with community-acquired bacteremia with mortality among patients with negative blood cultures and (2) determined the effects of bacteremia type and comorbidity level on mortality among patients with positive blood cultures.</p> <p>Methods</p> <p>This cohort study included 29,273 adults with blood cultures performed within the first 2 days following hospital admission to an internal medical ward in northern Denmark during 1995-2006. We computed product limit estimates and used Cox regression to compute adjusted mortality rate ratios (MRRs) within 0-2, 3-7, 8-30, and 31-180 days following admission for bacteremia patients compared to culture-negative patients.</p> <p>Results</p> <p>Mortality in 2,648 bacteremic patients and 26,625 culture-negative patients was 4.8% vs. 2.0% 0-2 days after admission, 3.7% vs. 2.7% 3-7 days after admission, 5.6% vs. 5.1% 8-30 days after admission, and 9.7% vs. 8.7% 31-180 days after admission, corresponding to adjusted MRRs of 1.9 (95% confidence interval (CI): 1.6-2.2), 1.1 (95% CI: 0.9-1.5), 0.9 (95% CI: 0.8-1.1), and 1.0 (95% CI: 0.8-1.1), respectively. Mortality was higher among patients with Gram-positive (adjusted 0-2-day MRR 1.9, 95% CI: 1.6-2.2) and polymicrobial bacteremia (adjusted 0-2-day MRR 3.5, 95% CI: 2.2-5.5) than among patients with Gram-negative bacteremia (adjusted 0-2-day MRR 1.5, 95% CI 1.2-2.0). After the first 2 days, patients with Gram-negative bacteremia had the same risk of dying as culture-negative patients (adjusted MRR 0.8, 95% CI: 0.5-1.1). Only patients with polymicrobial bacteremia had increased mortality within 31-180 days following admission (adjusted MRR 1.3, 95% CI: 0.8-2.1) compared to culture-negative patients. The association between blood culture status and mortality did not differ substantially by level of comorbidity.</p> <p>Conclusions</p> <p>Community-acquired bacteremia was associated with an increased risk of mortality in the first week of medical ward admission. Higher mortality among patients with Gram-positive and polymicrobial bacteremia compared with patients with Gram-negative bacteremia and negative cultures emphasizes the prognostic importance of these infections.</p

Assessing the accuracy of an inter-institutional automated patient-specific health problem list

<p>Abstract</p> <p>Background</p> <p>Health problem lists are a key component of electronic health records and are instrumental in the development of decision-support systems that encourage best practices and optimal patient safety. Most health problem lists require initial clinical information to be entered manually and few integrate information across care providers and institutions. This study assesses the accuracy of a novel approach to create an inter-institutional automated health problem list in a computerized medical record (MOXXI) that integrates three sources of information for an individual patient: diagnostic codes from medical services claims from all treating physicians, therapeutic indications from electronic prescriptions, and single-indication drugs.</p> <p>Methods</p> <p>Data for this study were obtained from 121 general practitioners and all medical services provided for 22,248 of their patients. At the opening of a patient's file, all health problems detected through medical service utilization or single-indication drug use were flagged to the physician in the MOXXI system. Each new arising health problem were presented as 'potential' and physicians were prompted to specify if the health problem was valid (Y) or not (N) or if they preferred to reassess its validity at a later time.</p> <p>Results</p> <p>A total of 263,527 health problems, representing 891 unique problems, were identified for the group of 22,248 patients. Medical services claims contributed to the majority of problems identified (77%), followed by therapeutic indications from electronic prescriptions (14%), and single-indication drugs (9%). Physicians actively chose to assess 41.7% (n = 106,950) of health problems. Overall, 73% of the problems assessed were considered valid; 42% originated from medical service diagnostic codes, 11% from single indication drugs, and 47% from prescription indications. Twelve percent of problems identified through other treating physicians were considered valid compared to 28% identified through study physician claims.</p> <p>Conclusion</p> <p>Automation of an inter-institutional problem list added over half of all validated problems to the health problem list of which 12% were generated by conditions treated by other physicians. Automating the integration of existing information sources provides timely access to accurate and relevant health problem information. It may also accelerate the uptake and use of electronic medical record systems.</p

Diagnostic accuracy of the Geriatric Depression Scale-30, Geriatric Depression Scale-15, Geriatric Depression Scale-5 and Geriatric Depression Scale-4 for detecting major depression : protocol for a systematic review and individual participant data meta-analysis

INTRODUCTION: The 30-item Geriatric Depression Scale (GDS-30) and the shorter GDS-15, GDS-5 and GDS-4 are recommended as depression screening tools for elderly individuals. Existing meta-analyses on the diagnostic accuracy of the GDS have not been able to conduct subgroup analyses, have included patients already identified as depressed who would not be screened in practice and have not accounted for possible bias due to selective reporting of results from only better-performing cut-offs in primary studies. Individual participant data meta-analysis (IPDMA), which involves a standard systematic review, then a synthesis of individual participant data, rather than summary results, could address these limitations. The objective of our IPDMA is to generate accuracy estimates to detect major depression for all possible cut-offs of each version of the GDS among studies using different reference standards, separately and among participant subgroups based on age, sex, dementia diagnosis and care settings. In addition, we will use a modelling approach to generate individual participant probabilities for major depression based on GDS scores (rather than a dichotomous cut-off) and participant characteristics (eg, sex, age, dementia status, care setting). METHODS AND ANALYSIS: Individual participant data comparing GDS scores to a major depression diagnosis based on a validated structured or semistructured diagnostic interview will be sought via a systematic review. Data sources will include Medline, Medline In-Process & Other Non-Indexed Citations, PsycINFO and Web of Science. Bivariate random-effects models will be used to estimate diagnostic accuracy parameters for each cut-off of the different versions of the GDS. Prespecified subgroup analyses will be conducted. Risk of bias will be assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. ETHICS AND DISSEMINATION: The findings of this study will be of interest to stakeholders involved in research, clinical practice and policy. PROSPERO REGISTRATION NUMBER: CRD42018104329

The impact of comorbidity and stage on ovarian cancer mortality: A nationwide Danish cohort study

<p>Abstract</p> <p>Background</p> <p>The incidence of ovarian cancer increases sharply with age, and many elderly patients have coexisting diseases. If patients with comorbidities are diagnosed with advanced stages, this would explain the poor survival observed among ovarian cancer patients with severe comorbidity. Our aims were to examine the prevalence of comorbidity according to stage of cancer at diagnosis, to estimate the impact of comorbidity on survival, and to examine whether the impact of comorbidity on survival varies by stage.</p> <p>Methods</p> <p>From the Danish Cancer Registry we identified 5,213 patients (> 15 years old) with ovarian cancer diagnosed from 1995 to 2003. We obtained information on comorbidities from the Danish National Hospital Discharge Registry. Vital status was determined through linkage to the Civil Registration System. We estimated the prevalence of comorbidity by stage and computed absolute survival and relative mortality rate ratios (MRRs) by comorbidity level (Charlson Index score 0, 1–2, 3+), using patients with Charlson Index score 0 as the reference group. We then stratified by stage and computed the absolute survival and MRRs according to comorbidity level, using patients with Charlson score 0 and localized tumour/FIGO I as the reference group. We adjusted for age and calendar time.</p> <p>Results</p> <p>Comorbidity was more common among patients with an advanced stage of cancer. One- and five-year survival was higher in patients without comorbidity than in patients with registered comorbidity. After adjustment for age and calendar time, one-year MRRs declined from 1.8 to 1.4 and from 2.7 to 2.0, for patients with Charlson scores 1–2 and 3+, respectively. After adjustment for stage, the MRRs further declined to 1.3 and 1.8, respectively. Five-year MRRs declined similarly after adjustment for age, calendar time, and stage. The impact of severe comorbidity on mortality varied by stage, particularly among patients with tumours with regional spread/FIGO-stages II and III.</p> <p>Conclusion</p> <p>The presence of severe comorbidity was associated with an advanced stage of ovarian cancer. Mortality was higher among patients with comorbidities and the impact of comorbidity varied by stage.</p

A classification of diabetic foot infections using ICD-9-CM codes: application to a large computerized medical database

<p>Abstract</p> <p>Background</p> <p>Diabetic foot infections are common, serious, and varied. Diagnostic and treatment strategies are correspondingly diverse. It is unclear how patients are managed in actual practice and how outcomes might be improved. Clarification will require study of large numbers of patients, such as are available in medical databases. We have developed and evaluated a system for identifying and classifying diabetic foot infections that can be used for this purpose.</p> <p>Methods</p> <p>We used the (VA) Diabetes Epidemiology Cohorts (DEpiC) database to conduct a retrospective observational study of patients with diabetic foot infections. DEpiC contains computerized VA and Medicare patient-level data for patients with diabetes since 1998. We determined which ICD-9-CM codes served to identify patients with different types of diabetic foot infections and ranked them in declining order of severity: Gangrene, Osteomyelitis, Ulcer, Foot cellulitis/abscess, Toe cellulitis/abscess, Paronychia. We evaluated our classification by examining its relationship to patient characteristics, diagnostic procedures, treatments given, and medical outcomes.</p> <p>Results</p> <p>There were 61,007 patients with foot infections, of which 42,063 were classifiable into one of our predefined groups. The different types of infection were related to expected patient characteristics, diagnostic procedures, treatments, and outcomes. Our severity ranking showed a monotonic relationship to hospital length of stay, amputation rate, transition to long-term care, and mortality.</p> <p>Conclusions</p> <p>We have developed a classification system for patients with diabetic foot infections that is expressly designed for use with large, computerized, ICD-9-CM coded administrative medical databases. It provides a framework that can be used to conduct observational studies of large numbers of patients in order to examine treatment variation and patient outcomes, including the effect of new management strategies, implementation of practice guidelines, and quality improvement initiatives.</p

Validation of a case definition to define chronic dialysis using outpatient administrative data

<p>Abstract</p> <p>Background</p> <p>Administrative health care databases offer an efficient and accessible, though as-yet unvalidated, approach to studying outcomes of patients with chronic kidney disease and end-stage renal disease (ESRD). The objective of this study is to determine the validity of outpatient physician billing derived algorithms for defining chronic dialysis compared to a reference standard ESRD registry.</p> <p>Methods</p> <p>A cohort of incident dialysis patients (Jan. 1 - Dec. 31, 2008) and prevalent chronic dialysis patients (Jan 1, 2008) was selected from a geographically inclusive ESRD registry and administrative database. Four administrative data definitions were considered: at least 1 outpatient claim, at least 2 outpatient claims, at least 2 outpatient claims at least 90 days apart, and continuous outpatient claims at least 90 days apart with no gap in claims greater than 21 days. Measures of agreement of the four administrative data definitions were compared to a reference standard (ESRD registry). Basic patient characteristics are compared between all 5 patient groups.</p> <p>Results</p> <p>1,118,097 individuals formed the overall population and 2,227 chronic dialysis patients were included in the ESRD registry. The three definitions requiring at least 2 outpatient claims resulted in kappa statistics between 0.60-0.80 indicating "substantial" agreement. "At least 1 outpatient claim" resulted in "excellent" agreement with a kappa statistic of 0.81.</p> <p>Conclusions</p> <p>Of the four definitions, the simplest (at least 1 outpatient claim) performed comparatively to other definitions. The limitations of this work are the billing codes used are developed in Canada, however, other countries use similar billing practices and thus the codes could easily be mapped to other systems. Our reference standard ESRD registry may not capture all dialysis patients resulting in some misclassification. The registry is linked to on-going care so this is likely to be minimal. The definition utilized will vary with the research objective.</p

Comparison of Rx-defined morbidity groups and diagnosis- based risk adjusters for predicting healthcare costs in Taiwan

<p>Abstract</p> <p>Background</p> <p>Medication claims are commonly used to calculate the risk adjustment for measuring healthcare cost. The Rx-defined Morbidity Groups (Rx-MG) which combine the use of medication to indicate morbidity have been incorporated into the Adjusted Clinical Groups (ACG) Case Mix System, developed by the Johns Hopkins University. This study aims to verify that the Rx-MG can be used for adjusting risk and for explaining the variations in the healthcare cost in Taiwan.</p> <p>Methods</p> <p>The Longitudinal Health Insurance Database 2005 (LHID2005) was used in this study. The year 2006 was chosen as the baseline to predict healthcare cost (medication and total cost) in 2007. The final sample size amounted to 793 239 (81%) enrolees, and excluded any cases with discontinued enrolment. Two different kinds of models were built to predict cost: the concurrent model and the prospective model. The predictors used in the predictive models included age, gender, Aggregated Diagnosis Groups (ADG, diagnosis- defined morbidity groups), and Rx-defined Morbidity Groups. Multivariate OLS regression was used in the cost prediction modelling.</p> <p>Results</p> <p>The concurrent model adjusted for Rx-defined Morbidity Groups for total cost, and controlled for age and gender had a better predictive R-square = 0.618, compared to the model adjusted for ADGs (R²= 0.411). The model combined with Rx-MGs and ADGs performed the best for concurrently predicting total cost (R²= 0.650). For prospectively predicting total cost, the model combined Rx-MGs and ADGs (R²= 0.382) performed better than the models adjusted by Rx-MGs (R²= 0.360) or ADGs (R²= 0.252) only. Similarly, the concurrent model adjusted for Rx-MGs predicting pharmacy cost had a better performance (R-square = 0.615), than the model adjusted for ADGs (R²= 0.431). The model combined with Rx-MGs and ADGs performed the best in concurrently as well as prospectively predicting pharmacy cost (R²= 0.638 and 0.505, respectively). The prospective models showed a remarkable improvement when adjusted by prior cost.</p> <p>Conclusions</p> <p>The medication-based Rx-Defined Morbidity Groups was useful in predicting pharmacy cost as well as total cost in Taiwan. Combining the information on medication and diagnosis as adjusters could arguably be the best method for explaining variations in healthcare cost.</p

Effect of a web-based chronic disease management system on asthma control and health-related quality of life: study protocol for a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Asthma is a prevalent and costly disease resulting in reduced quality of life for a large proportion of individuals. Effective patient self-management is critical for improving health outcomes. However, key aspects of self-management such as self-monitoring of behaviours and symptoms, coupled with regular feedback from the health care team, are rarely addressed or integrated into ongoing care. Health information technology (HIT) provides unique opportunities to facilitate this by providing a means for two way communication and exchange of information between the patient and care team, and access to their health information, presented in personalized ways that can alert them when there is a need for action. The objective of this study is to evaluate the acceptability and efficacy of using a web-based self-management system, My Asthma Portal (MAP), linked to a case-management system on asthma control, and asthma health-related quality of life.</p> <p>Methods</p> <p>The trial is a parallel multi-centered 2-arm pilot randomized controlled trial. Participants are randomly assigned to one of two conditions: a) MAP and usual care; or b) usual care alone. Individuals will be included if they are between 18 and 70, have a confirmed asthma diagnosis, and their asthma is classified as not well controlled by their physician. Asthma control will be evaluated by calculating the amount of fast acting beta agonists recorded as dispensed in the provincial drug database, and asthma quality of life using the Mini Asthma Related Quality of Life Questionnaire. Power calculations indicated a needed total sample size of 80 subjects. Data are collected at baseline, 3, 6, and 9 months post randomization. Recruitment started in March 2010 and the inclusion of patients in the trial in June 2010.</p> <p>Discussion</p> <p>Self-management support from the care team is critical for improving chronic disease outcomes. Given the high volume of patients and time constraints during clinical visits, primary care physicians have limited time to teach and reinforce use of proven self-management strategies. HIT has the potential to provide clinicians and a large number of patients with tools to support health behaviour change.</p> <p>Trial Registration</p> <p>Current Controlled Trials <a href="http://www.controlled-trials.com/ISRCTN34326236">ISRCTN34326236</a>.</p