Assessment of Hospital Characteristics and Interhospital Transfer Patterns of Adults With Emergency General Surgery Conditions.

Importance: Although patients with emergency general surgery (EGS) conditions frequently undergo interhospital transfers, the transfer patterns and associated factors are not well understood. Objective: To examine whether patients with EGS conditions are consistently directed to hospitals with more resources and better outcomes. Design, Setting, and Participants: This cohort study performed a network analysis of interhospital transfers among adults with EGS conditions from January 1 to December 31, 2016. The analysis used all-payer claims data from the 2016 Healthcare Cost and Utilization Project state inpatient and emergency department databases in 8 states. A total of 728 hospitals involving 85 415 transfers of 80 307 patients were included. Patients were eligible for inclusion if they were 18 years or older and had an acute care hospital encounter with a diagnosis of an EGS condition as defined by the American Association for the Surgery of Trauma. Data were analyzed from January 1, 2020, to June 17, 2021. Exposures: Hospital-level measures of size (total bed capacity), resources (intensive care unit [ICU] bed capacity, teaching status, trauma center designation, and presence of trauma and/or surgical critical care fellowships), EGS volume (annual EGS encounters), and EGS outcomes (risk-adjusted failure to rescue and in-hospital mortality). Main Outcomes and Measures: The main outcome was hospital-level centrality ratio, defined as the normalized number of incoming transfers divided by the number of outgoing transfers. A higher centrality ratio indicated more incoming transfers per outgoing transfer. Multivariable regression analysis was used to test the hypothesis that a higher hospital centrality ratio would be associated with more resources, higher volume, and better outcomes. Results: Among 80 307 total patients, the median age was 63 years (interquartile range [IQR], 50-75 years); 52.1% of patients were male and 78.8% were White. The median number of outgoing and incoming transfers per hospital were 106 (IQR, 61-157) and 36 (IQR, 8-137), respectively. A higher log-transformed centrality ratio was associated with more resources, such as higher ICU capacity (eg, >25 beds vs 0-10 beds: ß = 1.67 [95% CI, 1.16-2.17]; P < .001), and higher EGS volume (eg, quartile 4 [highest] vs quartile 1 [lowest]: ß = 0.78 [95% CI, 0-1.57]; P = .01). However, a higher log-transformed centrality ratio was not associated with better outcomes, such as lower in-hospital mortality (eg, quartile 4 [highest] vs quartile 1 [lowest]: ß = 0.30 [95% CI, -0.09 to 0.68]; P = .83) and lower failure to rescue (eg, quartile 4 [highest] vs quartile 1 [lowest]: ß = -0.50 [95% CI, -1.13 to 0.12]; P = .27). Conclusions and Relevance: In this study, EGS transfers were directed to high-volume hospitals with more resources but were not necessarily directed to hospitals with better clinical outcomes. Optimizing transfer destination in the interhospital transfer network has the potential to improve EGS outcomes.

Association between medical academic genealogy and publication outcome: impact of unconscious bias on scientific objectivity.

BACKGROUND: Our previous studies suggest that the training history of an investigator, termed "medical academic genealogy", influences the outcomes of that investigator's research. Here, we use meta-analysis and quantitative statistical modeling to determine whether such effects contribute to systematic bias in published conclusions. METHODS: A total of 108 articles were identified through a comprehensive search of the high-grade glioma (HGG) surgical resection literature. Analysis was performed on the 70 articles with sufficient data for meta-analysis. Pooled estimates were generated for key academic genealogies. Monte Carlo simulations were performed to determine whether the effects attributed to genealogy alone can arise due to chance alone. RESULTS: Meta-analysis of the HGG literature without consideration for academic medical genealogy revealed that gross total resection (GTR) was associated with a significant decrease in the odds ratio (OR) for the hazard of death after surgery for both anaplastic astrocytoma (AA) and glioblastoma (AA: log [OR] = - 0.04, 95% CI [- 0.07 to - 0.01]; glioblastoma log [OR] = - 0.36, 95% CI [- 0.44 to - 0.29]). For the glioblastoma literature, meta-analysis of articles contributed by members of a genealogy consisting of mostly radiation oncologists revealed no reduction in the hazard of death after GTR [log [OR] = - 0.16, 95% CI [- 0.41 to 0.09]. In contrast, meta-analysis of published articles contributed by members of a genealogy consisting of mostly neurosurgeons revealed that GTR was associated with a significant reduction in the hazard of death [log [OR] = - 0.29, 95% CI [- 0.40 to 0.18]. Monte Carlo simulation revealed that the observed discrepancy between the articles contributed by the members of these two genealogies was unlikely to arise by chance alone (p < 0.006). CONCLUSIONS: Meta-analysis of articles contributed by authors belonging to the different medical academic genealogies yielded distinct and contradictory pooled point-estimates, suggesting that genealogy contributes to systematic bias in the published literature.

Coevolution of Peer-Reviewed Literature and Clinical Practice in High-Grade Glioma Resection.

BACKGROUND: The paradigm of evidence-based medicine dictates that clinical practice should reflect the shifting landscape of the peer-reviewed literature. Here, we examined the extent to which this premise is fulfilled as it pertains to the surgical resection of high-grade gliomas (HGGs). OBJECTIVE: We assessed trends in published literature regarding HGG survival after resection in conjunction with trends in clinical practice patterns of HGG resection. METHODS: We performed a comprehensive PubMed search to identify articles that examined whether gross total resection (GTR) improves HGG survival. Temporal trends in the literature were compared with rates of GTR in the Surveillance Epidemiology and End Results (SEER) database, the Veterans Health Administration database, and published data series from academic neuro-oncology centers. RESULTS: Before 2000, the ratio of articles supporting survival benefit of GTR relative to those not supporting it ranged from approximately 1:5 to 1:1. Since 2000, this ratio has steadily increased such that by the post-2013 period, 32 of the 33 published articles (>30:1) supported the survival benefit of GTR. Although the frequency of GTR increased during the 2000-2004 period in the SEER and Veterans Health Administration database, no further increase in the frequency of GTR was observed thereafter. In contrast, resection rates in academic neuro-oncology centers continued to increase subsequent to 2004. CONCLUSIONS: Our results indicate that clinical practice patterns mirror publication patterns for HGG resection, suggesting that neurosurgical oncology is a field in which clinical practice is informed by the peer-reviewed literature.

'Journal Bias' in peer-reviewed literature: an analysis of the surgical high-grade glioma literature.

The core premise of evidence-based medicine is that clinical decisions are informed by the peer-reviewed literature. To extract meaningful conclusions from this literature, one must first understand the various forms of biases inherent within the process of peer review. We performed an exhaustive search that identified articles exploring the question of whether survival benefit was associated with maximal high-grade glioma (HGG) resection and analysed this literature for patterns of publication. We found that the distribution of these 108 articles among the 26 journals to be non-random (p<0.01), with 75 of the 108 published articles (69%) appearing in 6 of the 26 journals (25%). Moreover, certain journals were likely to publish a large number of articles from the same medical academic genealogy (authors with shared training history and/or mentor). We term the tendency of certain types of articles to be published in select journals 'journal bias' and discuss the implication of this form of bias as it pertains to evidence-based medicine.

Impact of medical academic genealogy on publication patterns: An analysis of the literature for surgical resection in brain tumor patients.

"Academic genealogy" refers to the linking of scientists and scholars based on their dissertation supervisors. We propose that this concept can be applied to medical training and that this "medical academic genealogy" may influence the landscape of the peer-reviewed literature. We performed a comprehensive PubMed search to identify US authors who have contributed peer-reviewed articles on a neurosurgery topic that remains controversial: the value of maximal resection for high-grade gliomas (HGGs). Training information for each key author (defined as the first or last author of an article) was collected (eg, author's medical school, residency, and fellowship training). Authors were recursively linked to faculty mentors to form genealogies. Correlations between genealogy and publication result were examined. Our search identified 108 articles with 160 unique key authors. Authors who were members of 2 genealogies (14% of key authors) contributed to 38% of all articles. If an article contained an authorship contribution from the first genealogy, its results were more likely to support maximal resection (log odds ratio = 2.74, p < 0.028) relative to articles without such contribution. In contrast, if an article contained an authorship contribution from the second genealogy, it was less likely to support maximal resection (log odds ratio = -1.74, p < 0.026). We conclude that the literature on surgical resection for HGGs is influenced by medical academic genealogies, and that articles contributed by authors of select genealogies share common results. These findings have important implications for the interpretation of scientific literature, design of medical training, and health care policy.

Using *ORA, a network analysis tool, to assess the relationship of handoffs to quality and safety outcomes.

Communication during patient handoffs has been widely implicated in patient safety issues. However, few studies have actually been able to quantify the relationship between handoffs and patient outcomes. We used *ORA, a dynamic network analysis tool, to examine handoffs between day and night shifts on seven units in three hospitals in the Southwest. Using *ORA's visualization and analysis capabilities, we examined the relationships between the handoff communication network metrics and a variety of patient safety quality and satisfaction outcomes. Unique network patterns were observed for different types of outcome variable (eg, safety, symptom management, self-care, and patient satisfaction). This exploratory project demonstrates the power of *ORA to identify communication patterns for large groups, such as patient care units. *ORA's network metrics can then be related to specific patient outcomes.

Simulating nursing unit performance with OrgAhead: strengths and challenges.

In this article, we briefly describe our use of a computational modeling tool, OrgAhead, details of which have been reported previously, then discuss several of the challenges computational modeling presented and our solutions. We used OrgAhead to simulate 39 nursing units in 13 Arizona hospitals and then predict changes to improve overall patient quality and safety outcomes. Creating the virtual units required (1) collecting data from managers, staff, patients, and quality and information services on each of the units; (2) mapping specific data elements (eg, control over nursing practice, nursingworkload, patient complexity, turbulence, orientation/tenure, education) to OrgAhead's parameters and variables; and then (3) validating that the newly created virtual units performed functionally like the actual units (eg, actual patient medication errors and fall rates correlated with the accuracy outcome variable in OrgAhead). Validation studies demonstrated acceptable correspondence between actual and virtual units. For all but the highest performing unit, we generated strategies that improved virtual performance and could reasonably be implemented on actual units to improve outcomes. Nurse managers, to whom we reported the results, responded positively to the unit-specific recommendations, which other methods cannot provide. In the end, resolving the modeling challenges we encountered has improved OrgAhead's functionality and usability.