S-Adenosylhomocysteine Is a Useful Metabolic Factor in the Early Prediction of Septic Disease Progression and Death in Critically Ill Patients: A Prospective Cohort Study.

A common final pathway of pathogenetic mechanisms in septic organ dysfunction and death is a lack or non-utilization of oxygen. Plasma concentrations of lactate serve as surrogates for the oxygen-deficiency-induced imbalance between energy supply and demand. As S-adenosylhomocysteine (SAH) was shown to reflect tissue hypoxia, we compared the ability of SAH versus lactate to predict the progression of inflammatory and septic disease to septic organ dysfunction and death. Using univariate and multiple logistic regression, we found that SAH but not lactate, taken upon patients' inclusion in the study close to ICU admission, significantly and independently contributed to the prediction of disease progression and death. Due to the stronger increase in SAH in relation to S-adenosylmethionine (SAM), the ratio of SAM to SAH, representing methylation potential, was significantly decreased in patients with septic organ dysfunction and non-survivors compared with SIRS/sepsis patients (2.8 (IQR 2.3-3.9) vs. 8.8 (4.9-13.8); p = 0.003) or survivors (4.9 (2.8-9.5) vs. 8.9 (5.1-14.3); p = 0.026), respectively. Thus, SAH appears to be a better contributor to the prediction of septic organ dysfunction and death than lactate in critically ill patients. As SAH is a potent inhibitor of SAM-dependent methyltransferases involved in numerous vital biochemical processes, the impairment of the SAM-to-SAH ratio in severely critically ill septic patients and non-survivors warrants further studies on the pathogenetic role of SAH in septic multiple organ failure.

Dissecting contributions of individual systemic inflammatory response syndrome criteria from a prospective algorithm to the prediction and diagnosis of sepsis in a polytrauma cohort.

Background: Sepsis is the leading cause of death in intensive care units (ICUs), and its timely detection and treatment improve clinical outcome and survival. Systemic inflammatory response syndrome (SIRS) refers to the concurrent fulfillment of at least two out of the following four clinical criteria: tachycardia, tachypnea, abnormal body temperature, and abnormal leukocyte count. While SIRS was controversially abandoned from the current sepsis definition, a dynamic SIRS representation still has potential for sepsis prediction and diagnosis. Objective: We retrospectively elucidate the individual contributions of the SIRS criteria in a polytrauma cohort from the post-surgical ICU of University Medical Center Mannheim (Germany). Methods: We used a dynamic and prospective SIRS algorithm tailored to the ICU setting by accounting for catecholamine therapy and mechanical ventilation. Two clinically relevant tasks are considered: (i) sepsis prediction using the first 24 h after admission to our ICU, and (ii) sepsis diagnosis using the last 24 h before sepsis onset and a time point of comparable ICU treatment duration for controls, respectively. We determine the importance of individual SIRS criteria by systematically varying criteria weights when summarizing the SIRS algorithm output with SIRS descriptors and assessing the classification performance of the resulting logistic regression models using a specifically developed ranking score. Results: Our models perform better for the diagnosis than the prediction task (maximum AUROC 0.816 vs. 0.693). Risk models containing only the SIRS level average mostly show reasonable performance across criteria weights, with prediction and diagnosis AUROCs ranging from 0.455 (weight on leukocyte criterion only) to 0.693 and 0.619 to 0.800, respectively. For sepsis prediction, temperature and tachypnea are the most important SIRS criteria, whereas the leukocytes criterion is least important and potentially even counterproductive. For sepsis diagnosis, all SIRS criteria are relevant, with the temperature criterion being most influential. Conclusion: SIRS is relevant for sepsis prediction and diagnosis in polytrauma, and no criterion should a priori be omitted. Hence, the original expert-defined SIRS criteria are valid, capturing important sepsis risk determinants. Our prospective SIRS algorithm provides dynamic determination of SIRS criteria and descriptors, allowing their integration in sepsis risk models also in other settings.

Pneumonia in the first week after polytrauma is associated with reduced blood levels of soluble herpes virus entry mediator.

Background: Pneumonia develops frequently after major surgery and polytrauma and thus in the presence of systemic inflammatory response syndrome (SIRS) and organ dysfunction. Immune checkpoints balance self-tolerance and immune activation. Altered checkpoint blood levels were reported for sepsis. We analyzed associations of pneumonia incidence in the presence of SIRS during the first week of critical illness and trends in checkpoint blood levels. Materials and methods: Patients were studied from day two to six after admission to a surgical intensive care unit (ICU). Blood was sampled and physician experts retrospectively adjudicated upon the presence of SIRS and Sepsis-1/2 every eight hours. We measured the daily levels of immune checkpoints and inflammatory markers by bead arrays for polytrauma patients developing pneumonia. Immune checkpoint time series were additionally determined for clinically highly similar polytrauma controls remaining infection-free during follow-up. We performed cluster analyses. Immune checkpoint time trends in cases and controls were compared with hierarchical linear models. For patients with surgical trauma and with and without sepsis, selected immune checkpoints were determined in study baseline samples. Results: In polytrauma patients with post-injury pneumonia, eleven immune checkpoints dominated subcluster 3 that separated subclusters 1 and 2 of myeloid markers from subcluster 4 of endothelial activation, tissue inflammation, and adaptive immunity markers. Immune checkpoint blood levels were more stable in polytrauma cases than controls, where they trended towards an increase in subcluster A and a decrease in subcluster B. Herpes virus entry mediator (HVEM) levels (subcluster A) were lower in cases throughout. In unselected surgical patients, sepsis was not associated with altered HVEM levels at the study baseline. Conclusion: Pneumonia development after polytrauma until ICU-day six was associated with decreased blood levels of HVEM. HVEM signaling may reduce pneumonia risk by strengthening myeloid antimicrobial defense and dampening lymphoid-mediated tissue damage. Future investigations into the role of HVEM in pneumonia and sepsis development and as a predictive biomarker should consider the etiology of critical illness and the site of infection.

Ground truth labels challenge the validity of sepsis consensus definitions in critical illness.

BACKGROUND: Sepsis is the leading cause of death in the intensive care unit (ICU). Expediting its diagnosis, largely determined by clinical assessment, improves survival. Predictive and explanatory modelling of sepsis in the critically ill commonly bases both outcome definition and predictions on clinical criteria for consensus definitions of sepsis, leading to circularity. As a remedy, we collected ground truth labels for sepsis. METHODS: In the Ground Truth for Sepsis Questionnaire (GTSQ), senior attending physicians in the ICU documented daily their opinion on each patient's condition regarding sepsis as a five-category working diagnosis and nine related items. Working diagnosis groups were described and compared and their SOFA-scores analyzed with a generalized linear mixed model. Agreement and discriminatory performance measures for clinical criteria of sepsis and GTSQ labels as reference class were derived. RESULTS: We analyzed 7291 questionnaires and 761 complete encounters from the first survey year. Editing rates for all items were > 90%, and responses were consistent with current understanding of critical illness pathophysiology, including sepsis pathogenesis. Interrater agreement for presence and absence of sepsis was almost perfect but only slight for suspected infection. ICU mortality was 19.5% in encounters with SIRS as the "worst" working diagnosis compared to 5.9% with sepsis and 5.9% with severe sepsis without differences in admission and maximum SOFA. Compared to sepsis, proportions of GTSQs with SIRS plus acute organ dysfunction were equal and macrocirculatory abnormalities higher (p < 0.0001). SIRS proportionally ranked above sepsis in daily assessment of illness severity (p < 0.0001). Separate analyses of neurosurgical referrals revealed similar differences. Discriminatory performance of Sepsis-1/2 and Sepsis-3 compared to GTSQ labels was similar with sensitivities around 70% and specificities 92%. Essentially no difference between the prevalence of SIRS and SOFA ≥ 2 yielded sensitivities and specificities for detecting sepsis onset close to 55% and 83%, respectively. CONCLUSIONS: GTSQ labels are a valid measure of sepsis in the ICU. They reveal suspicion of infection as an unclear clinical concept and refute an illness severity hierarchy in the SIRS-sepsis-severe sepsis spectrum. Ground truth challenges the accuracy of Sepsis-1/2 and Sepsis-3 in detecting sepsis onset. It is an indispensable intermediate step towards advancing diagnosis and therapy in the ICU and, potentially, other health care settings.

Maintaining oxygen delivery is crucial to prevent intestinal ischemia in critical ill patients.

BACKGROUND: Intestinal ischemia is a common complication with obscure pathophysiology in critically ill patients. Since insufficient delivery of oxygen is discussed, we investigated the influence of oxygen delivery, hemoglobin, arterial oxygen saturation, cardiac index and the systemic vascular resistance index on the development of intestinal ischemia. Furthermore, we evaluated the predictive power of elevated lactate levels for the diagnosis of intestinal ischemia. METHODS: In a retrospective case-control study data (mean oxygen delivery, minimum oxygen delivery, systemic vascular resistance index) of critical ill patients from 02/2009-07/2017 were analyzed using a proportional hazard model. General model fit and linearity were tested by likelihood ratio tests. The components of oxygen delivery (hemoglobin, arterial oxygen saturation and cardiac index) were individually tested in models. RESULTS: 59 out of 874 patients developed intestinal ischemia. A mean oxygen delivery less than 250ml/min/m2 (LRT vs. null model: p = 0.018; LRT for non-linearity: p = 0.012) as well as a minimum oxygen delivery less than 400ml/min/m2 (LRT vs null model: p = 0.016; LRT for linearity: p = 0.019) were associated with increased risk of the development of intestinal ischemia. We found no significant influence of hemoglobin, arterial oxygen saturation, cardiac index or systemic vascular resistance index. Receiver operating characteristics analysis for elevated lactate levels, pH, CO2 and central venous saturation was poor with an area under the receiver operating characteristic of 0.5324, 0.52, 0.6017 and 0.6786. CONCLUSION: There was a significant correlation for mean and minimum oxygen delivery with the incidence of intestinal ischemia for values below 250ml/min/m2 respectively 400ml/min/m2. Neither hemoglobin, arterial oxygen saturation, cardiac index, systemic vascular resistance index nor elevated lactate levels could be identified as individual risk factors.

Differential Network Testing Reveals Diverging Dynamics of Organ System Interactions for Survivors and Non-survivors in Intensive Care Medicine.

Statistical network analyses have become popular in many scientific disciplines, where an important task is to test for differences between two networks. We describe an overall framework for differential network testing procedures that vary regarding (1) the network estimation method, typically based on specific concepts of association, and (2) the network characteristic employed to measure the difference. Using permutation-based tests, our approach is general and applicable to various overall, node-specific or edge-specific network difference characteristics. The methods are implemented in our freely available R software package DNT, along with an R Shiny application. In a study in intensive care medicine, we compare networks based on parameters representing main organ systems to evaluate the prognosis of critically ill patients in the intensive care unit (ICU), using data from the surgical ICU of the University Medical Centre Mannheim, Germany. We specifically consider both cross-sectional comparisons between a non-survivor and a survivor group and longitudinal comparisons at two clinically relevant time points during the ICU stay: first, at admission, and second, at an event stage prior to death in non-survivors or a matching time point in survivors. The non-survivor and the survivor networks do not significantly differ at the admission stage. However, the organ system interactions of the survivors then stabilize at the event stage, revealing significantly more network edges, whereas those of the non-survivors do not. In particular, the liver appears to play a central role for the observed increased connectivity in the survivor network at the event stage.

Impact of different consensus definition criteria on sepsis diagnosis in a cohort of critically ill patients-Insights from a new mathematical probabilistic approach to mortality-based validation of sepsis criteria.

BACKGROUND: Sepsis-3 definition uses SOFA score to discriminate sepsis from uncomplicated infection, replacing SIRS criteria that were criticized for being inaccurate. Eligibility of sepsis-3 criteria for sepsis diagnosis and the applied validation methodology using mortality as endpoint are topic of ongoing debate. We assessed the impact of different criteria on sepsis diagnosis in our ICU and devised a mathematical approach for mortality-based validation of sepsis criteria. As infectious status is often unclear at clinical deterioration, we integrated non-infected patients into analysis. METHODS: Suspected infection, SOFA and SIRS were captured for an ICU cohort of a university center over one year. For raw scores (SIRS/SOFA) and sepsis criteria (SIRS≥2/SOFA≥2/SOFA_change≥2) frequencies and associations with in-hospital mortality were assessed. Using a mathematical approach, we estimated the correlation between sepsis and in-hospital mortality serving as reference for evaluation of observed mortality correlations of sepsis criteria. RESULTS: Of 791 patients, 369 (47%) were infected and 422 (53%) non-infected, with an in-hospital mortality of 39% and 15%. SIRS≥2 indicated sepsis in 90% of infected patients, SOFA≥2 in 99% and SOFA_change≥2 in 77%. In non-infected patients, SIRS, SOFA and SOFA_change were ≥2 in 78%, 88% and 58%. In AUROC analyses neither SOFA nor SIRS displayed superior mortality discrimination in infected compared to non-infected patients. The mathematically estimated correlation of sepsis and in-hospital mortality was 0.10 in infected and 0 in non-infected patients. Among sepsis criteria, solely SIRS≥2 agreed with expected correlations in both subgroups (infected: r = 0.19; non-infected: r = 0.02). CONCLUSIONS: SOFA≥2 yielded a more liberal sepsis diagnosis than SIRS≥2. None of the criteria showed an infection specific occurrence that would be essential for reliable sepsis detection. However, SIRS≥2 matched the mortality association pattern of a valid sepsis criterion, whereas SOFA-based criteria did not. With this study, we establish a mathematical approach to mortality-based evaluation of sepsis criteria.

Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction.

Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define ground truth sepsis labels as features of severity scoring models is inherently circular and compromises the validity of the proposed approaches. We propose to create an independent ground truth for sepsis research by exploiting implicit knowledge of clinical practitioners via an electronic questionnaire which records attending physicians' daily judgements of patients' sepsis status. We show that despite its small size, our dataset allows to achieve state-of-the-art AUROC scores. An inspection of learned weights for standardized features of the linear model lets us infer potentially surprising feature contributions and allows to interpret seemingly counterintuitive findings.

Is antibacterial treatment intensity lower in elderly patients? A retrospective cohort study in a German surgical intensive care unit.

BACKGROUND: Demographic change concurrent with medical progress leads to an increasing number of elderly patients in intensive care units (ICUs). Antibacterial treatment is an important, often life-saving, aspect of intensive care but burdened by the associated antimicrobial resistance risk. Elderly patients are simultaneously at greater risk of infections and may be more restrictively treated because, generally, treatment intensity declines with age. We therefore described utilization of antibacterials in ICU patients older and younger than 80 years and examined differences in the intensity of antibacterial therapy between both groups. METHODS: We analysed 17,464 valid admissions from the electronic patient data management system of our surgical ICU from April 2006 - October 2013. Antibacterial treatment rates were defined as days of treatment (exposed patient days) relative to patient days of ICU stay and calculated for old and young patients. Rates were compared in zero-inflated Poisson regression models adjusted for patients' sex, mean SAPS II- and TISS-scores, and calendar years yielding adjusted rate ratios (aRRs). Rate ratios exceeding 1 represent higher rates in old patients reflecting greater treatment intensity in old compared to younger patients. RESULTS: Observed antibacterial treatment rates were lower in patients 80 years and older compared to younger patients (30.97 and 39.73 exposed patient days per 100 patient days in the ICU, respectively). No difference in treatment intensity, however, was found from zero-inflated Poisson regression models permitting more adequate consideration of patient days with low treatment probability: for all antibacterials the adjusted rate ratio (aRR) was 1.02 (95%CI: 0.98-1.07). Treatment intensities were higher in elderly patients for penicillins (aRR 1.37 (95%CI: 1.26-1.48)), cephalosporins (aRR 1.20 (95%CI: 1.09-1.31)), carbapenems (aRR 1.35 (95%CI: 1.20-1.50)), fluoroquinolones (aRR 1.17 (95%CI: 1.05-1.30), and imidazoles (aRR 1.34 (95%CI: 1.23-1.46)). CONCLUSIONS: Elderly patients were generally less likely to be treated with antibacterials. This observation, however, did not persist in patients with comparable treatment probability. In these, antibacterial treatment intensity did not differ between younger and older ICU patients, for some antibacterial classes treatment intensity was even higher in the latter. Patient-level covariates are instrumental for a nuanced evaluation of age-effects in antibacterial treatment in the ICU.

Association of Microcirculation, Macrocirculation, and Severity of Illness in Septic Shock: A Prospective Observational Study to Identify Microcirculatory Targets Potentially Suitable for Guidance of Hemodynamic Therapy.

PURPOSE: Clinically unapparent microcirculatory impairment is common and has a negative impact on septic shock, but specific therapy is not established so far. This prospective observational study aimed at identifying candidate parameters for microcirculatory-guided hemodynamic therapy. ClinicalTrials.gov : NCT01530932. MATERIALS AND METHODS: Microcirculatory flow and postcapillary venous oxygen saturation were detected during vaso-occlusive testing (VOT) on days 1 (T0), 2 (T24), and 4 (T72) in 20 patients with septic shock at a surgical intensive care unit using a laser Doppler spectrophotometry system (O2C). RESULTS: Reperfusional maximal venous capillary oxygen saturation (SvcO2max) showed negative correlations with Simplified Acute Physiology Score II (SAPSII)/Sequential Organ Failure Assessment (SOFA) score, norepinephrine dosage, and lactate concentration and showed positive correlations with cardiac index (CI). At T24 and T72, SvcO2max was also inversely linked to fluid balance. With respect to any predictive value, SvcO2max and CI determined on day 1 (T0) were negatively correlated with SAPS II/SOFA on day 4 (T72). Moreover, SvcO2max measured on day 1 or day 2 was negatively correlated with cumulated fluid balance on day 4 ( r= -.472, P < .05 and r = -.829, P < .001). By contrast, CI neither on day 1 nor on day 2 was correlated with cumulated fluid balance on day 4 ( r = -.343, P = .17 and r = -.365, P = .15). CONCLUSION: In patients with septic shock, microcirculatory reserve as assessed by SvcO2max following VOT was impaired and negatively correlated with severity of illness and fluid balance. In contrast to CI, SvcO2max determined on day 1 or day 2 was significantly negatively correlated with cumulative fluid balance on day 4. Therefore, early microcirculatory measurement of SvcO2max might be superior to CI in guidance of sepsis therapy to avoid fluid overload. This has to be addressed in future clinical studies.

An Algorithm for Systemic Inflammatory Response Syndrome Criteria-Based Prediction of Sepsis in a Polytrauma Cohort.

OBJECTIVES: Lifesaving early distinction of infectious systemic inflammatory response syndrome, known as "sepsis," from noninfectious systemic inflammatory response syndrome is challenging in the ICU because of high systemic inflammatory response syndrome prevalence and lack of specific biomarkers. The purpose of this study was to use an automatic algorithm to detect systemic inflammatory response syndrome criteria (tachycardia, tachypnea, leukocytosis, and fever) in surgical ICU patients for ICU-wide systemic inflammatory response syndrome prevalence determination and evaluation of algorithm-derived systemic inflammatory response syndrome descriptors for sepsis prediction and diagnosis in a polytrauma cohort. DESIGN: Cross-sectional descriptive study and retrospective cohort study. SETTING: Electronic medical records of a tertiary care center's surgical ICU, 2006-2011. PATIENTS: All ICU admissions and consecutive polytrauma admissions. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Average prevalence of conventional systemic inflammatory response syndrome (≥ 2 criteria met concomitantly) from cross-sectional application of the algorithm to all ICU patients and each minute of the study period was 43.3%. Of 256 validated polytrauma patients, 85 developed sepsis (33.2%). Three systemic inflammatory response syndrome descriptors summarized the 24 hours after admission and before therapy initiation: 1) systemic inflammatory response syndrome criteria average for systemic inflammatory response syndrome quantification over time, 2) first-to-last minute difference for trend detection, and 3) change count reflecting systemic inflammatory response syndrome criteria fluctuation. Conventional systemic inflammatory response syndrome for greater than or equal to 1 minute had 91% sensitivity and 19% specificity, whereas a systemic inflammatory response syndrome criteria average cutoff value of 1.72 had 51% sensitivity and 77% specificity for sepsis prediction. For sepsis diagnosis, systemic inflammatory response syndrome criteria average and first-to-last minute difference combined yielded 82% sensitivity and 71% specificity compared with 99% sensitivity and only 31% specificity of conventional systemic inflammatory response syndrome from a nested case-control analysis. CONCLUSIONS: Dynamic systemic inflammatory response syndrome descriptors improved specificity of sepsis prediction and particularly diagnosis, rivaling established biomarkers, in a polytrauma cohort. They may enhance electronic sepsis surveillance once evaluated in other patient populations.

Performance of the 1 mg dexamethasone suppression test in patients with severe obesity.

OBJECTIVE: To analyze the performance of the 1 mg dexamethasone suppression test (DST) in patients with obesity. Special attention was paid to the influence of interfering medication on DST. METHODS: In this prospective cohort study (Mannheim Obesity Study), patients with obesity were evaluated before bariatric surgery. For evaluation of hypercortisolism, a 1 mg dexamethasone-suppression test (DST) in all subjects was performed. Medication was assessed for possible interference. RESULTS: Two hundred seventy-eight patients with a mean age of 42.3 years (68.8% women) and a mean BMI of 47.9 ± 8.4 kg/m(2) were screened. Insufficient suppression of cortisol after DST was found in 24 patients (8.6%). In two patients hypercortisolism was confirmed. The specificity for DST was calculated at 92.0%. Only CYP3A4 inducers (n = 22, 7.9%) and estrogen therapy (n = 17, 6.1%) were significantly associated with falsely elevated cortisol after DST. Regression analysis excluded any interrelation between DST and anthropometry. CONCLUSIONS: Low prevalence of hypercortisolism (0.7 or <1.8%) was found. Specificity of DST in this cohort typically screened for hypercortisolism was 92.0% (≤ 50 nmol/L). DST should be avoided in patients taking CYP3A4 inducers or estrogen therapy, due to their significant interaction. In summary, the 1 mg DST is an adequate test for screening for hypercortisolism even in patients with extreme obesity.

Agreement between administrative data and the Resident Assessment Instrument Minimum Dataset (RAI-MDS) for medication use in long-term care facilities: a population-based study.

BACKGROUND: Prescription medication use, which is common among long-term care facility (LTCF) residents, is routinely used to describe quality of care and predict health outcomes. Data sources that capture medication information, which include surveys, medical charts, administrative health databases, and clinical assessment records, may not collect concordant information, which can result in comparable prevalence and effect size estimates. The purpose of this research was to estimate agreement between two population-based electronic data sources for measuring use of several medication classes among LTCF residents: outpatient prescription drug administrative data and the Resident Assessment Instrument Minimum Data Set (RAI-MDS) Version 2.0. METHODS: Prescription drug and RAI-MDS data from the province of Saskatchewan, Canada (population 1.1 million) were linked for 2010/11 in this cross-sectional study. Agreement for anti-psychotic, anti-depressant, and anti-anxiety/hypnotic medication classes was examined using prevalence estimates, Cohen's κ, and positive and negative agreement. Mixed-effects logistic regression models tested resident and facility characteristics associated with disagreement. RESULTS: The cohort was comprised of 8,866 LTCF residents. In the RAI-MDS data, prevalence of anti-psychotics was 35.7%, while for anti-depressants it was 37.9% and for hypnotics it was 27.1%. Prevalence was similar in prescription drug data for anti-psychotics and anti-depressants, but lower for hypnotics (18.0%). Cohen's κ ranged from 0.39 to 0.85 and was highest for the first two medication classes. Diagnosis of a mood disorder and facility affiliation was associated with disagreement for hypnotics. CONCLUSIONS: Agreement between prescription drug administrative data and RAI-MDS assessment data was influenced by the type of medication class, as well as selected patient and facility characteristics. Researchers should carefully consider the purpose of their study, whether it is to capture medication that are dispensed or medications that are currently used by residents, when selecting a data source for research on LTCF populations.

Validity of the RAI-MDS for ascertaining diabetes and comorbid conditions in long-term care facility residents.

BACKGROUND: This study assessed the validity of the Resident Assessment Instrument Minimum Data Set (RAI-MDS) Version 2.0 for diagnoses of diabetes and comorbid conditions in residents of long-term care facilities (LTCFs). METHODS: Hospital inpatient, outpatient physician billing, RAI-MDS, and population registry data for 1997 to 2011 from Saskatchewan, Canada were used to ascertain cases of diabetes and 12 comorbid conditions. Prevalence estimates were calculated for both RAI-MDS and administrative health data. Sensitivity, specificity, and positive and negative predictive values (PPV and NPV) were calculated using population-based administrative health data as the validation data source. Cohen's κ was used to estimate agreement between the two data sources. RESULTS: 23,217 LTCF residents were in the diabetes case ascertainment cohort. Diabetes prevalence was 25.3% in administrative health data and 21.9% in RAI-MDS data. Overall sensitivity of a RAI-MDS diabetes diagnoses was 0.79 (95% CI: 0.79, 0.80) and the PPV was 0.92 (95% CI: 0.91, 0.92), when compared to administrative health data. Sensitivity of the RAI-MDS for ascertaining comorbid conditions ranged from 0.21 for osteoporosis to 0.92 for multiple sclerosis; specificity was high for most conditions. CONCLUSIONS: RAI-MDS clinical assessment data are sensitive to ascertain diabetes cases in LTCF populations when compared to administrative health data. For many comorbid conditions, RAI-MDS data have low validity when compared to administrative data. Risk-adjustment measures based on these comorbidities might not produce consistent results for RAI-MDS and administrative health data, which could affect the conclusions of studies about health outcomes and quality of care across facilities.

Antibacterial drugs and the risk of community-associated methicillin-resistant Staphylococcus aureus in children.

OBJECTIVE: To investigate in children the association between antibacterial drugs and subsequent diagnosis of methicillin-resistant Staphylococcus aureus (MRSA) in the community. DESIGN: Population-based case-control study in children 1 to 19 years of age. SETTING: Primary care, General Practice Research Database, United Kingdom, 1994-2007. PARTICIPANTS: Cases were children who had MRSA diagnosed as outpatients, and controls were individually matched on age and practice, with the matched case's diagnosis date as the index date for both. Main Exposures Antibacterial agents prescribed 180 to 30 days prior to the index date, excluding prescriptions 30 days before the index date to prevent protopathic bias. OUTCOME MEASURES: Rate ratios (RRs) estimated from the odds ratios of exposure in cases compared with controls using conditional logistic regression, adjusted for comorbid conditions, other prescription drug use, and hospitalization. RESULTS: The rate of MRSA was 4.5 per 100,000 per year. Of 297 cases and 9357 controls, 52.5% and 13.6%, respectively, received antibacterial drug prescriptions during the 150-day exposure window. The adjusted RR with any antibacterial drug was 3.5 (95% confidence interval [CI], 2.6-4.8). The RRs increased with the number of prescriptions (2.2 [95% CI, 1.5-3.2], 3.3 [95% CI, 1.9-5.6], 11.0 [95% CI, 5.6-21.6], and 18.2 [95% CI, 9.4-35.4] for 1, 2, 3, and ≥4 prescriptions, respectively). The RR was particularly elevated for quinolones at 14.8 (95% CI, 3.9-55.8), with wide variation among antibacterial classes. CONCLUSION: While close to half of children were diagnosed as having MRSA in the community without prior antibacterial drugs, such agents are associated with a dose-dependent increased risk, concordant with findings in adults.

Secular trends of antibacterial prescribing in UK paediatric primary care.

BACKGROUND: Resistance to antibacterial drugs can be contained by judicious prescribing. In particular, the use of these drugs in children requires ongoing surveillance. While there was a decline in antibacterial prescribing in the UK during the 1990s, recent trends are less well known. OBJECTIVES: To describe antibiotic prescribing patterns and time trends in children in the UK over the last two decades. METHODS: We identified all children ages 0-19 years from 1993 to 2007 and their antibiotic prescriptions from the General Practice Research Database. We used Poisson regression to estimate prescription rates considering the children's age and gender, calendar year and practice. RESULTS: The cohort included 1 751 645 children with 5 835 891 antibacterial prescriptions. The average prescription rate was 511 prescriptions per 1000 person-years [95% confidence interval (CI) 509-513]. As of 1995, the rate decreased to 419/1000 person-years (95% CI 411-426) in 2000, then increased to 568/1000 person-years (95% CI 559-577) in 2007. Between 2000 and 2007, rates increased on average by 4.3% (95% CI 3.7-5.0%) annually, amounting to an increase of 40.7% (95% CI 34.5-47.2%) for all children. Rates were generally higher in girls, except for boys <5 years. Broad-spectrum penicillins were most frequently prescribed; their rate increased on average by 4.6% annually (95% CI 4.0-5.3%) after 2000. This trend was similar in most classes of antibacterials. CONCLUSIONS: Antibacterial prescribing to outpatient children in the UK has been steadily increasing since 2000, consistently for boys and girls, across all ages and antibacterial classes.