Optimizing Interhospital Transfers in Orthopedics and Trauma Surgery: Challenges, Insights, and Proposals for Standardized Care in Germany.

BACKGROUND: Germany's high density of under-equipped hospitals and anticipated surge in orthopedic and trauma surgery-related diseases by 2030, combined with personnel shortages, are expected to increase patient transfers between hospitals, an issue that urgently needs standardized protocols. Despite some existing cooperative agreements, such as between joint-replacement centers or within the Trauma Network DGU®, these measures do not adequately address the full range of patient-transfer cases, including those due to a lack of specialization or staff shortages, resulting in delayed treatment and potential health risks. This study aims to dissect the intricacies of interhospital transfers in orthopedics and trauma surgery across Germany, focusing on understanding the underlying reasons for transfers, comparing the operational structures of small and large hospitals, and laying the groundwork for future standardized protocols to enhance patient care. MATERIAL AND METHODS: A cross-sectional study was conducted in the form of an online survey via SoSci Survey, which was directed at orthopedic surgeons and trauma surgeons working in hospitals in Germany. The 22-question survey gathered information on participants' clinic roles, departmental details, transfer processes, frequent diagnoses, perceptions of transfer quality, and improvement areas. The survey was sent to orthopedic and trauma surgeons in Germany by the specialist society. The data were analyzed using descriptive and inferential statistics to ensure a comprehensive insight into interhospital transfer practices. RESULTS: The study involved 152 participants from various hospital ranks and located in different hospital sizes and types across rural and urban areas. A significant difference was observed between the care structures of basic/regular care and central/maximum care hospitals, especially regarding the available facilities and specialties. These findings suggest improvements such as better patient documentation, increased digital communication, optimized patient distribution, and standardization of transfer requests, among others. CONCLUSIONS: This study highlights the urgent need for improved protocols and resource allocation to eliminate inequalities in transfers between hospitals in orthopedics and trauma surgery in Germany.

It is (not) always on Friday: inter-hospital patient transfers in orthopedic and trauma surgery.

BACKGROUND: While inter-hospital transfers for patients who have suffered major trauma have been well investigated, patient flows for other injured patients, or cases with orthopedic complications, are rarely described. This study aims to analyze the affected collective and to show possible reasons, patterns, and pitfalls to optimize the process in future. MATERIALS AND METHODS: In a prospective cohort study, all consecutive transfers to a Level I trauma center in Germany were documented and assessed. Patients suffering a major trauma were excluded. Data on the primary treating hospital, patient characteristics, and differences between emergency and elective surgery were analyzed. RESULTS: A total of 227 patients were included; 162 were injured, while 65 had suffered a complication after elective orthopedic surgery or had a complex orthopedic pathology. The most common diagnoses leading to transfer were pathologies of the extremities (n = 62), pathologies of the spine (n = 50), and infections (n = 18). The main reasons stated by the transferring hospitals were a lack of expertise (137 cases) and a lack of capacity (43 cases). There was a significantly higher rate of transfers due to trauma (n = 162) than for orthopedic patients (n = 65), p < 0.0001. CONCLUSION: There is currently no structured procedure or algorithm for transferring patients in orthopedics and trauma surgery.

Meta-analysis under imbalance in measurement of confounders in cohort studies using only summary-level data.

BACKGROUND: Cohort collaborations often require meta-analysis of exposure-outcome association estimates across cohorts as an alternative to pooling individual-level data that requires a laborious process of data harmonization on individual-level data. However, it is likely that important confounders are not all measured uniformly across the cohorts due to differences in study protocols. This imbalance in measurement of confounders leads to association estimates that are not comparable across cohorts and impedes the meta-analysis of results. METHODS: In this article, we empirically show some asymptotic relations between fully adjusted and unadjusted exposure-outcome effect estimates, and provide theoretical justification for the same. We leverage these results to obtain fully adjusted estimates for the cohorts with no information on confounders by borrowing information from cohorts with complete measurement on confounders. We implement this novel method in CIMBAL (confounder imbalance), which additionally provides a meta-analyzed estimate that appropriately accounts for the dependence between estimates arising due to borrowing of information across cohorts. We perform extensive simulation experiments to study CIMBAL's statistical properties. We illustrate CIMBAL using National Children's Study (NCS) data to estimate association of maternal education and low birth weight in infants, adjusting for maternal age at delivery, race/ethnicity, marital status, and income. RESULTS: Our simulation studies indicate that estimates of exposure-outcome association from CIMBAL are closer to the truth than those from commonly-used approaches for meta-analyzing cohorts with disparate confounder measurements. CIMBAL is not too sensitive to heterogeneity in underlying joint distributions of exposure, outcome and confounders but is very sensitive to heterogeneity of confounding bias across cohorts. Application of CIMBAL to NCS data for a proof-of-concept analysis further illustrates the utility and advantages of CIMBAL. CONCLUSIONS: CIMBAL provides a practical approach for meta-analyzing cohorts with imbalance in measurement of confounders under a weak assumption that the cohorts are independently sampled from populations with the same confounding bias.