Technique Failure in a Multicenter Canadian Home Hemodialysis Cohort.

RATIONALE & OBJECTIVE: Increasing uptake of home hemodialysis (HD) has led to interest in characteristics that predict discontinuation of home HD therapy for reasons other than death or transplantation. Recent reports of practice pattern variability led to the hypothesis that there are patient- and center-specific factors that influence these discontinuations. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: Incident home HD patients at 7 centers in Canada between 2000 and 2010. PREDICTOR: Treatment center, case-mix, and process-of-care variables. OUTCOMES: Technique failure (defined as discontinuation of home HD therapy for any reason other than training failure, death, or transplantation) and mortality. ANALYTICAL APPROACH: Regression modeling of technique failure using Cox proportional hazard models adjusting for treatment center and modifiable and nonmodifiable patient-level variables, censored for death and transplantation. RESULTS: The cohort consisted of 579 patients. Mean age was 49.9±14.1 years, 74% were of European ancestry, median dialysis vintage was 1.9 (IQR, 0.6-5.2) years, and 68% used an arteriovenous access. Mean duration of dialysis was 31.2±12.6 hours per week. Unadjusted 1- and 2-year technique survival and overall survival were 90% and 83% and 94% and 87%, respectively. Treating center was a strong predictor of technique failure and mortality, with HRs ranging from 0.37 to 5.11 for technique failure (1 of 6 centers with P<0.05 relative to the reference) and 0.17 to 8.73 for mortality (3 of 6 centers with P<0.05 relative to the reference). With baseline adjustment for center, only older age and more than 3 treatments per week remained significant predictors of technique failure, while no individual-level variables remained as significant predictors of survival. LIMITATIONS: Limited statistical power. CONCLUSIONS: Home HD treating centers may influence technique failure and patient mortality independent of case-mix. The relationship between processes of care and patient outcomes requires further investigation.

A log-Weibull spatial scan statistic for time to event data.

BACKGROUND: Spatial scan statistics have been used for the identification of geographic clusters of elevated numbers of cases of a condition such as disease outbreaks. These statistics accompanied by the appropriate distribution can also identify geographic areas with either longer or shorter time to events. Other authors have proposed the spatial scan statistics based on the exponential and Weibull distributions. RESULTS: We propose the log-Weibull as an alternative distribution for the spatial scan statistic for time to events data and compare and contrast the log-Weibull and Weibull distributions through simulation studies. The effect of type I differential censoring and power have been investigated through simulated data. Methods are also illustrated on time to specialist visit data for discharged patients presenting to emergency departments for atrial fibrillation and flutter in Alberta during 2010-2011. We found northern regions of Alberta had longer times to specialist visit than other areas. CONCLUSIONS: We proposed the spatial scan statistic for the log-Weibull distribution as a new approach for detecting spatial clusters for time to event data. The simulation studies suggest that the test performs well for log-Weibull data.

A comparative evaluation of the strengths of association between different emergency department crowding metrics and repeat visits within 72 hours.

OBJECTIVE: We sought to compare strengths of association among multiple emergency department (ED) input, throughput and output metrics and the outcome of 72-h ED re-visits. METHODS: This database analysis used healthcare administrative data from three urban, university-affiliated EDs in Calgary, Canada, calendar years 2010-2014. We used data from all patients presenting to participating EDs during the study period, and the primary analysis was performed on patients discharged from the ED. Regression models quantified the association between input, throughput and output metrics and the risk of return ED visit within 72 h of discharge from the index ED encounter. Strength of association between the crowding metrics and 72-h ED re-visits was compared using Akaike's Information Criterion. RESULTS: The findings of this study are based on data from 845,588 patient encounters ending in discharge. The input metric with the strongest association with 72-h re-visits was median ED waiting time. The throughput metric with the strongest association with 72-h re-visits was the ED occupancy. The output metric with the strongest association with 72-h re-visits was the median inpatient boarding time. CONCLUSION: Input, throughput and output metrics are all associated with 72-h re-visits. Delays in any of these operational phases have detrimental effects on patient outcomes. ED waiting time, ED occupancy, and boarding times are the most meaningful input, throughput and output metrics. These should be the preferred metrics for quantifying ED crowding in research and quality improvement efforts, and for clinicians to monitor ED crowding in real time.

RéSUMé: OBJECTIF: Nous avons cherché à comparer la force de l'association entre plusieurs paramètres d'entrée, de débit et de sortie des services d'urgence (SU) et l'issue des nouvelles visites aux SU après 72 heures. MéTHODES: Cette analyse de base de données a utilisé des données administratives sur les soins de santé de trois services d'urgence urbains affiliés à une université à Calgary, au Canada, pour les années civiles 2010-2014. Nous avons utilisé les données de tous les patients se présentant aux urgences participantes pendant la période de l'étude, et l'analyse primaire a porté sur les patients sortis des urgences. Des modèles de régression ont quantifié l'association entre les paramètres d'entrée, de débit et de sortie et le risque d'une nouvelle visite aux urgences dans les 72 heures suivant la sortie des urgences de référence. La force de l'association entre les paramètres d'encombrement et les réadmissions aux urgences à 72 heures a été comparée à l'aide du critère d'information d'Akaike. RéSULTATS: Les résultats de cette étude sont basés sur les données de 845 588 rencontres de patients se terminant par une sortie. La mesure d'entrée présentant la plus forte association avec les nouvelles visites dans les 72 heures était le temps d'attente médian aux urgences. La mesure du débit avec la plus forte association avec les visites répétées de 72 heures était l'occupation par le SU. La métrique de sortie présentant la plus forte association avec les revisites à 72 heures était la durée médiane d'embarquement des patients hospitalisés. CONCLUSIONS: Les mesures d'entrée, de débit et de sortie sont toutes associées aux revisites de 72 heures. Les retards dans l'une de ces phases opérationnelles ont des effets néfastes sur les résultats pour les patients. Le temps d'attente aux urgences, le taux d'occupation des urgences et le temps d'embarquement sont les paramètres les plus significatifs en termes d'entrée, de débit et de sortie. Ces paramètres devraient être privilégiés pour quantifier l'encombrement des urgences dans le cadre de la recherche et des efforts d'amélioration de la qualité, et pour permettre aux cliniciens de surveiller l'encombrement des urgences en temps réel.