Defining Posterior Wall Fragments in Associated Both Column Acetabular Fractures (OTA/AO 62C).

OBJECTIVE: Associated both column acetabular fractures (OTA/AO 62C) with concomitant posterior wall fracture fragments (ABC + PW) have not been well-defined. The purpose of this study was to report on the incidence and morphology of ABC + PW fractures. METHODS: A retrospective review of associated both column (ABC) fractures between 2014 and 2020 was performed. Computed tomography scans including 3-D surface rendered reformats for each were reviewed to determine whether a posterior wall (PW) fragment was present and its morphologic characteristics. RESULTS: One hundred fifty-two ABC fractures were identified. Sixty-two fractures (41%) were identified as ABC + PW. 3D-computed tomographies were available on 58 fractures. Morphologic analysis was performed based on the relationship of the fracture to the gluteal pillar. Twenty PW fragments were posterior to the gluteal pillar, 19 extended into the gluteal pillar, and 19 extended anterior. Fifty-two fractures were treated with operative fixation; 32 (62%) were clamped and fixed with screws from the same anterior approach whereas 15 (29%) required a separate posterior approach; and no fixation was placed in 5 (9%). 29 of 32 PW fragments (91%) requiring fixation that extended into or anterior to the pillar were fixed from the anterior approach, and 7 of 15 posterior fractures (47%) required a separate posterior approach. CONCLUSIONS: A separate PW fragment was identified in 41% of ABC fractures. Their variation in morphology can be classified into 3 types based on the relation to the gluteal pillar that has potential implications for treatment from the anterior approach compared with requiring a separate posterior approach. We suggest these data could be used to update the 2018 OTA/AO Fracture Compendium. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

The Fallacy of Follow-up: When Orthopaedic Trauma Patients Actually Return to Clinic.

BACKGROUND: Clinical follow-up in orthopaedic trauma is challenging, yet expectations exist that a 1-year follow-up is the minimum requirement for clinical trials and research publications. The primary purpose of our study was to evaluate the rate of follow-up after operative orthopaedic trauma care and the relationship to clinical care. Our secondary aim was to identify any independent risk factors regarding follow-up completion. METHODS: A chart review of patients operatively treated for a traumatic injury during the months of January and July 2016 was conducted. Patient demographic characteristics, injury type, severity, and patient distance from the hospital were collected. The final clinical instructions and whether a return visit was requested or as needed were recorded. RESULTS: There were 293 patients in this study, of whom 84 (29%) had follow-up of at least 1 year and 52 (18%) were instructed to follow up only as needed at their last visit prior to the 1-year mark. When removing the latter 52 patients, the 1-year follow-up rate was 35% (84 of 241 patients). Of these 241 patients, 157 (65%) were requested to return for additional clinical care but failed to return prior to 1 year. Logistic regression identified tobacco use (odds ratio [OR], 0.34 [95% confidence interval (CI), 0.15 to 0.77]; p = 0.010), final appointment status (OR, 6.3 [95% CI, 3.4 to 11.6]; p < 0.001), isolated compared with multiple fractures (OR, 2.2 [95% CI, 1.2 to 4.1]; p = 0.013), and distance from the trauma center per mile as a continuous variable (OR, 0.999 [95% CI, 0.998 to 1.0]; p = 0.03) as significant predictors. CONCLUSIONS: Our data suggest that a 1-year clinic follow-up requirement may not be feasible. We observed a low rate of patients with a minimum 1-year clinical follow-up. Clinical care had been completed in 18% of patients prior to 1 year. Journal and grant reviewers may need to consider the feasibility and clinical relevance of these follow-up expectations.

Utilizing simulated errors in radiotherapy plans to quantify the effectiveness of the physics plan review.

PURPOSE: The review of a radiation therapy plan by a physicist prior to treatment is a standard tool for ensuring the quality of treatments. However, little is known about how well this task is performed in practice. The goal of this study is to present a novel method to measure the effectiveness of physics plan review by introducing simulated errors into computerized "mock" treatment charts and measuring the performance of plan review by physicists. METHODS: We generated six simulated treatment charts containing multiple errors. To select errors, we compiled a list based on events from a departmental incident learning system and an international incident learning system (SAFRON). Seventeen errors with the highest scores for frequency and severity were included in the simulations included six mock treatment charts. Eight physicists reviewed the simulated charts as they would a normal pretreatment plan review, with each chart being reviewed by at least six physicists. There were 113 data points for evaluation. Observer bias was minimized using a simple error vs hidden error approach, using detectability scores for stratification. The confidence interval for the proportion of errors detected was computed using the Wilson score interval. RESULTS: Simulated errors were detected in 67% of reviews [58-75%] (95% confidence interval [CI] in brackets). Of the errors included in the simulated plans, the following error scenarios had the highest detection rates: an incorrect isocenter in DRR (93% [70-99%]), a planned dose different from the prescribed dose (92% [67-99%]) and invalid QA (85% [58-96%]). Errors with low detection rates included incorrect CT dataset (0%, [0-39%]) and incorrect isocenter localization in planning system (38% [18-64%]). Detection rates of errors from simulated charts were compared against observed detection rates of errors from a departmental incident learning system. CONCLUSIONS: It has been notoriously difficult to quantify error and safety performance in oncology. This study uses a novel technique of simulated errors to quantify performance and suggests that the pretreatment physics plan review identifies some errors with high fidelity while other errors are more challenging to detect. These data will guide future work on standardization and automation. The example process studied here was physics plan review, but this approach of simulated errors may be applied in other contexts as well and may also be useful for training and education purposes.

The effectiveness of pretreatment physics plan review for detecting errors in radiation therapy.

PURPOSE: The pretreatment physics plan review is a standard tool for ensuring treatment quality. Studies have shown that the majority of errors in radiation oncology originate in treatment planning, which underscores the importance of the pretreatment physics plan review. This quality assurance measure is fundamentally important and central to the safety of patients and the quality of care that they receive. However, little is known about its effectiveness. The purpose of this study was to analyze reported incidents to quantify the effectiveness of the pretreatment physics plan review with the goal of improving it. METHODS: This study analyzed 522 potentially severe or critical near-miss events within an institutional incident learning system collected over a three-year period. Of these 522 events, 356 originated at a workflow point that was prior to the pretreatment physics plan review. The remaining 166 events originated after the pretreatment physics plan review and were not considered in the study. The applicable 356 events were classified into one of the three categories: (1) events detected by the pretreatment physics plan review, (2) events not detected but "potentially detectable" by the physics review, and (3) events "not detectable" by the physics review. Potentially detectable events were further classified by which specific checks performed during the pretreatment physics plan review detected or could have detected the event. For these events, the associated specific check was also evaluated as to the possibility of automating that check given current data structures. For comparison, a similar analysis was carried out on 81 events from the international SAFRON radiation oncology incident learning system. RESULTS: Of the 356 applicable events from the institutional database, 180/356 (51%) were detected or could have been detected by the pretreatment physics plan review. Of these events, 125 actually passed through the physics review; however, only 38% (47/125) were actually detected at the review. Of the 81 events from the SAFRON database, 66/81 (81%) were potentially detectable by the pretreatment physics plan review. From the institutional database, three specific physics checks were particularly effective at detecting events (combined effectiveness of 38%): verifying the isocenter (39/180), verifying DRRs (17/180), and verifying that the plan matched the prescription (12/180). The most effective checks from the SAFRON database were verifying that the plan matched the prescription (13/66) and verifying the field parameters in the record and verify system against those in the plan (23/66). Software-based plan checking systems, if available, would have potential effectiveness of 29% and 64% at detecting events from the institutional and SAFRON databases, respectively. CONCLUSIONS: Pretreatment physics plan review is a key safety measure and can detect a high percentage of errors. However, the majority of errors that potentially could have been detected were not detected in this study, indicating the need to improve the pretreatment physics plan review performance. Suggestions for improvement include the automation of specific physics checks performed during the pretreatment physics plan review and the standardization of the review process.

Targeting safety improvements through identification of incident origination and detection in a near-miss incident learning system.

PURPOSE: Radiation treatment planning involves a complex workflow that has multiple potential points of vulnerability. This study utilizes an incident reporting system to identify the origination and detection points of near-miss errors, in order to guide their departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or applied a near-miss risk index (NMRI) to gauge severity. METHODS: From 3/2012 to 3/2014, 1897 incidents were analyzed from a departmental incident learning system. All incidents were prospectively reviewed weekly by a multidisciplinary team and assigned a NMRI score ranging from 0 to 4 reflecting potential harm to the patient (no potential harm to potential critical harm). Incidents were classified by point of incident origination and detection based on a 103-step workflow. The individual steps were divided among nine broad workflow categories (patient assessment, imaging for radiation therapy (RT) planning, treatment planning, pretreatment plan review, treatment delivery, on-treatment quality management, post-treatment completion, equipment/software quality management, and other). The average NMRI scores of incidents originating or detected within each broad workflow area were calculated. Additionally, out of 103 individual process steps, 35 were classified as safety barriers, the process steps whose primary function is to catch errors. The safety barriers which most frequently detected incidents were identified and analyzed. Finally, the distance between event origination and detection was explored by grouping events by the number of broad workflow area events passed through before detection, and average NMRI scores were compared. RESULTS: Near-miss incidents most commonly originated within treatment planning (33%). However, the incidents with the highest average NMRI scores originated during imaging for RT planning (NMRI = 2.0, average NMRI of all events = 1.5), specifically during the documentation of patient positioning and localization of the patient. Incidents were most frequently detected during treatment delivery (30%), and incidents identified at this point also had higher severity scores than other workflow areas (NMRI = 1.6). Incidents identified during on-treatment quality management were also more severe (NMRI = 1.7), and the specific process steps of reviewing portal and CBCT images tended to catch highest-severity incidents. On average, safety barriers caught 46% of all incidents, most frequently at physics chart review, therapist's chart check, and the review of portal images; however, most of the incidents that pass through a particular safety barrier are not designed to be capable of being captured at that barrier. CONCLUSIONS: Incident learning systems can be used to assess the most common points of error origination and detection in radiation oncology. This can help tailor safety improvement efforts and target the highest impact portions of the workflow. The most severe near-miss events tend to originate during simulation, with the most severe near-miss events detected at the time of patient treatment. Safety barriers can be improved to allow earlier detection of near-miss events.

Can emergent treatments result in more severe errors?: An analysis of a large institutional near-miss incident reporting database.

PURPOSE: Emergent radiation treatments may be subject to more errors because of the compressed time frame. Few data exist on the magnitude of this problem or how to guide safety improvement interventions. The purpose of this study is to examine patterns of near-miss events in emergent treatments using a large institutional incident reporting system. METHODS AND MATERIALS: Events in the incident reporting database from February 2012 to October 2013 were reviewed prospectively by a multidisciplinary team to identify emergent treatments. Reports were scored for potential near-miss risk index (NMRI) on a 0 to 4 scale. Workflow steps of where events originated and were detected were analyzed. Events were categorized by use of the causal factor system from the Radiation Oncology Incident Learning System. Mann-Whitney U tests were used to compare mean NMRI score, and Fisher exact tests were performed to compare the proportion of high-risk events between emergent and nonemergent treatments and between emergent treatments on weekdays and weekends or holidays. RESULTS: Over the study period, approximately 1600 patients were treated, 190 of them emergently. Seventy-one incident reports were submitted for 55 unique patients. Fewer events were reported for emergent treatments than for nonemergent treatments (0.37 events per new treatment vs 0.86; P < .01). Mean risk index for emergent reports was 1.90 versus 1.48 for nonemergent reports (P < .01). Rate of NMRI 4 was 10% for emergent treatments versus 4% for nonemergent treatments (P < .01). Emergent treatments started on a weekend or holiday had a higher proportion of critical near-miss events than emergent treatments started during the week (37% vs 7.9%, P = .034). CONCLUSIONS: In this study, fewer near-miss incidents were reported per treatment course for emergent treatments. This may be attributable to reporting bias. More importantly, when emergent near misses occur, they are of greater severity.

Metrics of success: Measuring impact of a departmental near-miss incident learning system.

PURPOSE: There is a growing interest in the application of incident learning systems (ILS) to radiation oncology. The purpose of the present study is to define statistical metrics that may serve as benchmarks for successful operation of an incident learning system. METHODS AND MATERIALS: A departmental safety and quality ILS was developed to monitor errors, near-miss events, and process improvement suggestions. Event reports were reviewed by a multiprofessional quality improvement committee. Events were scored by a near-miss risk index (NMRI) and categorized by event point of origination and discovery. Reporting trends were analyzed over a 2-year period, including total number and rates of events reported, users reporting, NMRI, and event origination and discovery. RESULTS: A total of 1897 reports were evaluated (1.0 reports/patient, 0.9 reports/unique treatment course). Participation in the ILS increased as demonstrated by total events (2.1 additional reports/month) and unique users (0.5 new users/month). Sixteen percent of reports had an NMRI of 0 (none), 42% had an NMRI of 1 (mild), 25% had an NMRI of 2 (moderate), 12% had an NMRI of 3 (severe), and 5% had an NMRI of 4 (critical). Event NMRI showed a significant decrease in the first 6 months (1.68-1.42, P < .001). Trends in origination and discovery of reports were broadly distributed between radiation therapy process steps and staff groups. The highest risk events originated in imaging for treatment planning (NMRI = 2.0 ± 1.1; P < .0001) and were detected in on-treatment quality management (NMRI = 1.7 ± 1.1; P = .003). CONCLUSIONS: Over the initial 2-year period of ILS operation, rates of reporting increased, staff participation increased, and NMRI of reported events declined. These data mirror previously reported findings of improvement in safety culture endpoints. These metrics may be useful for other institutions seeking to create or evaluate their own ILS.

Measurable improvement in patient safety culture: A departmental experience with incident learning.

PURPOSE: Rigorous use of departmental incident learning is integral to improving patient safety and quality of care. The goal of this study was to quantify the impact of a high-volume, departmental incident learning system on patient safety culture. METHODS AND MATERIALS: A prospective, voluntary, electronic incident learning system was implemented in February 2012 with the intent of tracking near-miss/no-harm incidents. All incident reports were reviewed weekly by a multiprofessional team with regular department-wide feedback. Patient safety culture was measured at baseline with validated patient safety culture survey questions. A repeat survey was conducted after 1 and 2 years of departmental incident learning. Proportional changes were compared by χ(2) or Fisher exact test, where appropriate. RESULTS: Between 2012 and 2014, a total of 1897 error/near-miss incidents were reported, representing an average of 1 near-miss report per patient treated. Reports were filed by a cross section of staff, with the majority of incidents reported by therapists, dosimetrists, and physicists. Survey response rates at baseline and 1 and 2 years were 78%, 80%, and 80%, respectively. Statistically significant and sustained improvements were noted in several safety metrics, including belief that the department was openly discussing ways to improve safety, the sense that reports were being used for safety improvement, and the sense that changes were being evaluated for effectiveness. None of the surveyed dimensions of patient safety culture worsened. Fewer punitive concerns were noted, with statistically significant decreases in the worry of embarrassment in front of colleagues and fear of getting colleagues in trouble. CONCLUSIONS: A comprehensive incident learning system can identify many areas for improvement and is associated with significant and sustained improvements in patient safety culture. These data provide valuable guidance as incident learning systems become more widely used in radiation oncology.