Beating the Clock: A Prediction Model for Timely Care in Emergency Departments.

We developed and validated a statistical prediction model using 2.5 electronic health records from 24 German emergency departments (EDs) to estimate treatment timeliness at triage. The model's moderate fit and reliance on interoperable, routine data suggest its potential for implementation in ED crowding management.

Data Integration for a Global Deep-Phenotyping Registry for Pulmonary Hypertension - Lessons Learned.

The integration of data from various healthcare centers into disease registries is pivotal for facilitating collaborative research and enhancing clinical insights. In this study, we investigate the integration process of existing registries into the PVRI GoDeep meta-registry, focusing on the complexities and challenges encountered. We detail the integration process, including data transformation, mapping updates, and feedback mechanisms. Our findings underscore the importance of standardized processes and proactive communication in addressing data quality issues, ultimately enhancing the reliability and trustworthiness of meta-registry data. Through careful harmonization of the data and transparent documentation of data processing, we pave the way for leveraging registry data to drive advancements in pulmonary hypertension research and patient care.

The Effects of Displaying the Time Targets of the Manchester Triage System to Emergency Department Personnel: Prospective Crossover Study.

BACKGROUND: The use of triage systems such as the Manchester Triage System (MTS) is a standard procedure to determine the sequence of treatment in emergency departments (EDs). When using the MTS, time targets for treatment are determined. These are commonly displayed in the ED information system (EDIS) to ED staff. Using measurements as targets has been associated with a decline in meeting those targets. OBJECTIVE: This study investigated the impact of displaying time targets for treatment to physicians on processing times in the ED. METHODS: We analyzed the effects of displaying time targets to ED staff on waiting times in a prospective crossover study, during the introduction of a new EDIS in a large regional hospital in Germany. The old information system version used a module that showed the time target determined by the MTS, while the new system version used a priority list instead. Evaluation was based on 35,167 routinely collected electronic health records from the preintervention period and 10,655 records from the postintervention period. Electronic health records were extracted from the EDIS, and data were analyzed using descriptive statistics and generalized additive models. We evaluated the effects of the intervention on waiting times and the odds of achieving timely treatment according to the time targets set by the MTS. RESULTS: The average ED length of stay and waiting times increased when the EDIS that did not display time targets was used (average time from admission to treatment: preintervention phase=median 15, IQR 6-39 min; postintervention phase=median 11, IQR 5-23 min). However, severe cases with high acuity (as indicated by the triage score) benefited from lower waiting times (0.15 times as high as in the preintervention period for MTS1, only 0.49 as high for MTS2). Furthermore, these patients were less likely to receive delayed treatment, and we observed reduced odds of late treatment when crowding occurred. CONCLUSIONS: Our results suggest that it is beneficial to use a priority list instead of displaying time targets to ED personnel. These time targets may lead to false incentives. Our work highlights that working better is not the same as working faster.

Comparison of Contemporary Risk Scores in All Groups of Pulmonary Hypertension: A Pulmonary Vascular Research Institute GoDeep Meta-Registry Analysis.

BACKGROUND: Pulmonary hypertension (PH) is a heterogeneous disease with a poor prognosis. Accurate risk stratification is essential for guiding treatment decisions in pulmonary arterial hypertension (PAH). Although various risk models have been developed for PAH, their comparative prognostic potential requires further exploration. Additionally, the applicability of risk scores in PH groups beyond group 1 remains to be investigated. RESEARCH QUESTION: Are risk scores originally developed for PAH predictive in PH groups 1 through 4? STUDY DESIGN AND METHODS: We conducted a comprehensive analysis of outcomes among patients with incident PH enrolled in the multicenter worldwide Pulmonary Vascular Research Institute GoDeep meta-registry. Analyses were performed across PH groups 1 through 4 and further subgroups to evaluate the predictive value of PAH risk scores, including Registry to Evaluate Early and Long-Term PAH Disease Mangement (REVEAL) Lite 2, REVEAL 2.0, ESC/ERS 2022, Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) 3-strata, and COMPERA 4-strata. RESULTS: Eight thousand five hundred sixty-five patients were included in the study, of whom 3,537 patients were assigned to group 1 PH, whereas 1,807 patients, 1,635 patients, and 1,586 patients were assigned to group 2 PH, group 3 PH, and group 4 PH, respectively. Pulmonary hemodynamics were impaired with median mean pulmonary arterial pressure of 42 mm Hg (interquartile range, 33-52 mm Hg) and pulmonary vascular resistance of 7 Wood units (WU) (interquartile range, 4-11 WU). All risk scores were prognostic in the entire PH population and in each of the PH groups 1 through 4. The REVEAL scores, when used as continuous prediction models, demonstrated the highest statistical prognostic power and granularity; the COMPERA 4-strata risk score provided subdifferentiation of the intermediate-risk group. Similar results were obtained when separately analyzing various subgroups (PH subgroups 1.1, 1.4.1, and 1.4.4; PH subgroups 3.1 and 3.2; group 2 with isolated postcapillary PH vs combined precapillary and postcapillary PH; patients of all groups with concomitant cardiac comorbidities; and severe [> 5 WU] vs nonsevere PH). INTERPRETATION: This comprehensive study with real-world data from 15 PH centers showed that PAH-designed risk scores possess predictive power in a large PH cohort, whether considered as common to the group or calculated separately for each PH group (1-4) and various subgroups. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT05329714; URL: www. CLINICALTRIALS: gov.