Development of Red Flags Index for Early Referral of Adults with Symptoms and Signs Suggestive of Crohn's Disease: An IOIBD Initiative

International audience; BACKGROUND AND AIMS:Diagnostic delay is frequent in patients with Crohn's disease (CD). We developed a tool to predict early diagnosis.METHODS:A systematic literature review and 12 CD specialists identified 'Red Flags', i.e. symptoms or signs suggestive of CD. A 21-item questionnaire was administered to 36 healthy subjects, 80 patients with irritable bowel syndrome (non-CD group) and 85 patients with recently diagnosed (<18 months) CD. Patients with CD were asked to recall symptoms and signs they experienced during the 12 months before diagnosis. Multiple logistic regression analyses selected and weighted independent items to construct the Red Flags index. A receiver operating characteristic curve was used to assess the threshold that discriminated CD from non-CD. Association with the Red Flags index relative to this threshold was expressed as the odds ratios (OR).RESULTS:Two hundred and one subjects, CD and non-CD, answered the questionnaire. The multivariate analysis identified eight items independently associated with a diagnosis of CD. A minimum Red Flags index value of 8 was highly predictive of CD diagnosis with sensitivity and specificity bootstrap estimates of 0.94 (95% confidence interval 0.88-0.99) and 0.94 (0.90-0.97), respectively. Positive and negative likelihood ratios were 15.1 (9.3-33.6) and 0.066 (0.013-0.125), respectively. The association between CD diagnosis and a Red Flags index value of ≥8 corresponds to an OR of 290 (p < 0.0001).CONCLUSIONS:The Red Flags index using early symptoms and signs has high predictive value for the diagnosis of CD. These results need prospective validation prior to introduction into clinical practice

Metachronous peritoneal metastases in patients with pT4b colon cancer: An international multicenter analysis of intraperitoneal versus retroperitoneal tumor invasion

It was hypothesized that colon cancer with only retroperitoneal invasion is associated with a low risk of peritoneal dissemination. This study aimed to compare the risk of metachronous peritoneal metastases (mPM) between intraperitoneal and retroperitoneal invasion

Do coder characteristics influence validity of ICD-10 hospital discharge data?

<p>Abstract</p> <p>Background</p> <p>Administrative data are widely used to study health systems and make important health policy decisions. Yet little is known about the influence of coder characteristics on administrative data validity in these studies. Our goal was to describe the relationship between several measures of validity in coded hospital discharge data and 1) coders' volume of coding (≥13,000 vs. <13,000 records), 2) coders' employment status (full- vs. part-time), and 3) hospital type.</p> <p>Methods</p> <p>This descriptive study examined 6 indicators of face validity in ICD-10 coded discharge records from 4 hospitals in Calgary, Canada between April 2002 and March 2007. Specifically, mean number of coded diagnoses, procedures, complications, Z-codes, and codes ending in 8 or 9 were compared by coding volume and employment status, as well as hospital type. The mean number of diagnoses was also compared across coder characteristics for 6 major conditions of varying complexity. Next, kappa statistics were computed to assess agreement between discharge data and linked chart data reabstracted by nursing chart reviewers. Kappas were compared across coder characteristics.</p> <p>Results</p> <p>422,618 discharge records were coded by 59 coders during the study period. The mean number of diagnoses per record decreased from 5.2 in 2002/2003 to 3.9 in 2006/2007, while the number of records coded annually increased from 69,613 to 102,842. Coders at the tertiary hospital coded the most diagnoses (5.0 compared with 3.9 and 3.8 at other sites). There was no variation by coder or site characteristics for any other face validity indicator. The mean number of diagnoses increased from 1.5 to 7.9 with increasing complexity of the major diagnosis, but did not vary with coder characteristics. Agreement (kappa) between coded data and chart review did not show any consistent pattern with respect to coder characteristics.</p> <p>Conclusions</p> <p>This large study suggests that coder characteristics do not influence the validity of hospital discharge data. Other jurisdictions might benefit from implementing similar employment programs to ours, e.g.: a requirement for a 2-year college training program, a single management structure across sites, and rotation of coders between sites. Limitations include few coder characteristics available for study due to privacy concerns.</p

A comparison between the APACHE II and Charlson Index Score for predicting hospital mortality in critically ill patients

<p>Abstract</p> <p>Background</p> <p>Risk adjustment and mortality prediction in studies of critical care are usually performed using acuity of illness scores, such as Acute Physiology and Chronic Health Evaluation II (APACHE II), which emphasize physiological derangement. Common risk adjustment systems used in administrative datasets, like the Charlson index, are entirely based on the presence of co-morbid illnesses. The purpose of this study was to compare the discriminative ability of the Charlson index to the APACHE II in predicting hospital mortality in adult multisystem ICU patients.</p> <p>Methods</p> <p>This was a population-based cohort design. The study sample consisted of adult (>17 years of age) residents of the Calgary Health Region admitted to a multisystem ICU between April 2002 and March 2004. Clinical data were collected prospectively and linked to hospital outcome data. Multiple regression analyses were used to compare the performance of APACHE II and the Charlson index.</p> <p>Results</p> <p>The Charlson index was a poor predictor of mortality (C = 0.626). There was minimal difference between a baseline model containing age, sex and acute physiology score (C = 0.74) and models containing either chronic health points (C = 0.76) or Charlson index variations (C = 0.75, 0.76, 0.77). No important improvement in prediction occurred when the Charlson index was added to the full APACHE II model (C = 0.808 to C = 0.813).</p> <p>Conclusion</p> <p>The Charlson index does not perform as well as the APACHE II in predicting hospital mortality in ICU patients. However, when acuity of illness scores are unavailable or are not recorded in a standard way, the Charlson index might be considered as an alternative method of risk adjustment and therefore facilitate comparisons between intensive care units.</p

Comparing comorbidity measures for predicting mortality and hospitalization in three population-based cohorts

<p>Abstract</p> <p>Background</p> <p>Multiple comorbidity measures have been developed for risk-adjustment in studies using administrative data, but it is unclear which measure is optimal for specific outcomes and if the measures are equally valid in different populations. This research examined the predictive performance of five comorbidity measures in three population-based cohorts.</p> <p>Methods</p> <p>Administrative data from the province of Saskatchewan, Canada, were used to create the cohorts. The general population cohort included all Saskatchewan residents 20+ years, the diabetes cohort included individuals 20+ years with a diabetes diagnosis in hospital and/or physician data, and the osteoporosis cohort included individuals 50+ years with diagnosed or treated osteoporosis. Five comorbidity measures based on health services utilization, number of different diagnoses, and prescription drugs over one year were defined. Predictive performance was assessed for death and hospitalization outcomes using measures of discrimination (<it>c</it>-statistic) and calibration (Brier score) for multiple logistic regression models.</p> <p>Results</p> <p>The comorbidity measures with optimal performance were the same in the general population (<it>n </it>= 662,423), diabetes (<it>n </it>= 41,925), and osteoporosis (<it>n </it>= 28,068) cohorts. For mortality, the Elixhauser index resulted in the highest <it>c</it>-statistic and lowest Brier score, followed by the Charlson index. For hospitalization, the number of diagnoses had the best predictive performance. Consistent results were obtained when we restricted attention to the population 65+ years in each cohort.</p> <p>Conclusions</p> <p>The optimal comorbidity measure depends on the health outcome and not on the disease characteristics of the study population.</p

Nurse forecasting in Europe (RN4CAST): Rationale, design and methodology

Contains fulltext : 97171.pdf (postprint version ) (Open Access)BACKGROUND: Current human resources planning models in nursing are unreliable and ineffective as they consider volumes, but ignore effects on quality in patient care. The project RN4CAST aims innovative forecasting methods by addressing not only volumes, but quality of nursing staff as well as quality of patient care. METHODS/DESIGN: A multi-country, multilevel cross-sectional design is used to obtain important unmeasured factors in forecasting models including how features of hospital work environments impact on nurse recruitment, retention and patient outcomes. In each of the 12 participating European countries, at least 30 general acute hospitals were sampled. Data are gathered via four data sources (nurse, patient and organizational surveys and via routinely collected hospital discharge data). All staff nurses of a random selection of medical and surgical units (at least 2 per hospital) were surveyed. The nurse survey has the purpose to measure the experiences of nurses on their job (e.g. job satisfaction, burnout) as well as to allow the creation of aggregated hospital level measures of staffing and working conditions. The patient survey is organized in a sub-sample of countries and hospitals using a one-day census approach to measure the patient experiences with medical and nursing care. In addition to conducting a patient survey, hospital discharge abstract datasets will be used to calculate additional patient outcomes like in-hospital mortality and failure-to-rescue. Via the organizational survey, information about the organizational profile (e.g. bed size, types of technology available, teaching status) is collected to control the analyses for institutional differences.This information will be linked via common identifiers and the relationships between different aspects of the nursing work environment and patient and nurse outcomes will be studied by using multilevel regression type analyses. These results will be used to simulate the impact of changing different aspects of the nursing work environment on quality of care and satisfaction of the nursing workforce. DISCUSSION: RN4CAST is one of the largest nurse workforce studies ever conducted in Europe, will add to accuracy of forecasting models and generate new approaches to more effective management of nursing resources in Europe

Organizational configuration of hospitals succeeding in attracting and retaining nurses

Organizational configuration of hospitals succeeding in attracting and retaining nurses. This paper contrasts structural and managerial characteristics of low- and high-turnover hospitals, and describes the organizational configuration of attractive hospitals. In countries facing nurse shortages and turnover, some hospitals succeed in recruiting and retaining nurses. In Magnet Hospitals, managerial practices and environmental characteristics increase nurses\u2019 job satisfaction and their commitment to the organization, which in turn decreases nurse turnover. Such an approach suggests that organizations are best understood as clusters of interconnected structures and practices, i.e. organizational configurations rather than entities whose components can be understood in isolation. From a sample of 12 hospitals whose nurse turnover was studied for 1 year, structural and organizational features of hospitals in the first and fourth quartiles, i.e. attractive (turnover11\uc68%) were contrasted. A questionnaire, including perceptions of health-related factors, job demands, stressors, work schedules, organizational climate, and work adjustments antecedent to turnover, was received from 401 nurses working in attractive hospitals (response rate - 53\uc68%) and 774 nurses in conventional hospitals (response rate \ubc 54\uc65%). Structural characteristics did not differentiate attractive and conventional hospitals, but employee perceptions towards the organization differed strikingly. Differences were observed for risk exposure, emotional demands, role ambiguity and conflicts, work-family conflicts, effort-reward imbalance and the meaning of work, all in favour of attractive hospitals (P < 0.01). Relationships with nursing management, work ability and satisfaction with working time, handover shifts and schedules were also better in attractive hospitals (P < 0.001). Job satisfaction and commitment were higher in attractive hospitals, whereas burnout and intention to leave were lower (P < 0.001). Organizational characteristics are key factors in nurse attraction and retention. Nurses face difficulties in their work situations, but some hospitals are perceived as healthy organizations. The concept of attractive institutions could serve as a catalyst for improvement in nurses\u2019 work environments in Europe