Delivering Impactful Scientific Oral Presentations.

This Viewpoint highlights specific challenges of facing a scientific conference audience and provides practical recommendations to overcome these challenges.

Practical guide to building machine learning-based clinical prediction models using imbalanced datasets.

Clinical prediction models often aim to predict rare, high-risk events, but building such models requires robust understanding of imbalance datasets and their unique study design considerations. This practical guide highlights foundational prediction model principles for surgeon-data scientists and readers who encounter clinical prediction models, from feature engineering and algorithm selection strategies to model evaluation and design techniques specific to imbalanced datasets. We walk through a clinical example using readable code to highlight important considerations and common pitfalls in developing machine learning-based prediction models. We hope this practical guide facilitates developing and critically appraising robust clinical prediction models for the surgical community.

The burden of readmissions after rib fractures among older adults.

BACKGROUND: The index hospitalization morbidity and mortality of rib fractures among older adults (aged ≥65 years) is well-known, yet the burden and risks for readmissions after rib fractures in this vulnerable population remain understudied. We aimed to characterize the burdens and etiologies associated with 3-month readmissions among older adults who suffer rib fractures. We hypothesized that readmissions would be common and associated with modifiable etiologies. METHODS: This survey-weighted retrospective study using the 2017 and 2019 National Readmissions Database evaluated adults aged ≥65 years hospitalized with multiple rib fractures and without major extrathoracic injuries. The main outcome was the proportion of patients experiencing all-cause 3-month readmissions. We assessed the 5 leading principal readmission diagnoses overall and delineated them by index hospitalization discharge disposition (home or facility). Sensitivity analysis using clinical classification categories characterized readmissions that could reasonably represent rib fracture-related sequelae. RESULTS: In 2017, 25,092 patients met the inclusion criteria, with 20% (N = 4,894) experiencing 3-month readmissions. Six percent of patients did not survive their readmission. The 5 leading principal readmission diagnoses were sepsis (many associated with secondary diagnoses of pneumonia [41%] or urinary tract infections [41%]), hypertensive heart/kidney disease, hemothorax, pneumonia, and respiratory failure. In 2019, a comparable 3-month readmission rate of 23% and identical 5 leading diagnoses were found. Principal readmission diagnosis of hemothorax was associated with the shortest time to readmission (median [interquartile range]:9 [5-23] days). Among patients discharged home after index hospitalization, pleural effusion-possibly representing mischaracterized hemothorax-was among the leading principal readmission diagnoses. Some patients readmitted with a principal diagnosis of hemothorax or pleural effusion had these diagnoses at index hospitalization; a lower proportion of these patients underwent pleural fluid intervention during index hospitalization compared with readmission. On sensitivity analysis, 30% of 3-month readmissions were associated with principal diagnoses suggesting rib fracture-related sequelae. CONCLUSION: Readmissions are not infrequent among older adults who suffer rib fractures, even in the absence of major extrathoracic injuries. Future studies should better characterize how specific complications associated with readmissions, such as pneumonia, urinary tract infections, and delayed hemothoraces, could be mitigated.

Serratus anterior plane block improves pain and incentive spirometry volumes in trauma patients with multiple rib fractures: a prospective cohort study.

Background: Rib fractures are common injuries associated with considerable morbidity, long-term disability, and mortality. Early, adequate analgesia is important to mitigate complications such as pneumonia and respiratory failure. Regional anesthesia has been proposed for rib fracture pain control due to its superior side effect profile compared with systemic analgesia. Our objective was to evaluate the effect of emergency physician-performed, ultrasound-guided serratus anterior plane block (SAPB) on pain and respiratory function in emergency department patients with multiple acute rib fractures. Methods: This was a prospective observational cohort study of adult patients at a level 1 trauma center who had two or more acute unilateral rib fractures. Eligible patients received a SAPB if an emergency physician trained in the procedure was available at the time of diagnosis. Primary outcomes were the absolute change in pain scores and percent change in expected incentive spirometry volumes from baseline to 3 hours after rib fracture diagnosis. Results: 38 patients met eligibility criteria, 15 received the SAPB and 23 did not. The SAPB group had a greater decrease in pain scores at 3 hours (-3.7 vs. -0.9; p=0.003) compared with the non-SAPB group. The SAPB group also had an 11% (CI 1.5% to 17%) increase in percent expected spirometry volumes at 3 hours which was significantly better than the non-SAPB group, which had a -3% (CI -9.1% to 2.7%) decrease (p=0.008). Conclusion: Patients with rib fractures who received SAPB as part of a multimodal pain control strategy had a greater improvement in pain and respiratory function compared with those who did not. Larger trials are indicated to assess the generalizability of these initial findings.

Pediatric Trauma Center Access, Regional Injury Burden, and Socioeconomic Disadvantage.

This cross-sectional study assesses whether populations in socioeconomically disadvantaged regions in the US lack timely access to pediatric trauma centers.

Career Trajectory After General Surgery Residency: Do Academic Program Graduates Pursue Academic Surgery?

OBJECTIVE: Determine the proportion of contemporary US academic general surgery residency program graduates who pursue academic careers and identify factors associated with pursuing academic careers. SUMMARY BACKGROUND DATA: Many academic residency programs aim to cultivate academic surgeons, yet the proportion of contemporary graduates who choose academic careers is unclear. The potential determinants that affect graduates' decisions to pursue academic careers remain underexplored. METHODS: We collected program and individual-level data on 2015 and 2018 graduates across 96 US academic general surgery residency programs using public resources. We defined those pursuing academic careers as faculty within US allopathic medical school-affiliated surgery departments who published two or more peer-reviewed publications as the first or senior author between 2020-2021. After variable selection using sample splitting LASSO regression, multivariable regression evaluated association with pursuing academic careers among all graduates, and graduates of top-20 residency programs. Secondary analysis using multivariable ordinal regression explored factors associated with high research productivity during early faculty years. RESULTS: Among 992 graduates, 166 (17%) were pursuing academic careers according to our definition. Graduating from a top-20 ranked residency program (OR[95%CI]: 2.34[1.40-3.88]), working with a longitudinal research mentor during residency (OR[95%CI]: 2.21[1.24-3.95]), holding an advanced degree (OR[95%CI]: 2.20[1.19-3.99]), and the number of peer-reviewed publications during residency as first or senior author (OR[95%CI]: 1.13[1.07-1.20]) were associated with pursuing an academic surgery career, while the number of peer-reviewed publications before residency was not (OR[95%CI]: 1.08[0.99-1.20]). Among top 20 program graduates, working with a longitudinal research mentor during residency (OR[95%CI]: 0.95[0.43-2.09]) was not associated with pursuing an academic surgery career. The number of peer-reviewed publications during residency as first or senior author was the only variable associated with higher productivity during early faculty years (OR[95%CI]: 1.12[1.07-1.18]). CONCLUSIONS: Our findings suggest programs that aim to graduate academic surgeons may benefit from ensuring trainees receive infrastructural support and demonstrate sustained commitment to research throughout residency. Our results should be interpreted cautiously as the impact of unmeasured confounders is unclear.

Access to Burn Care in the US.

This cross-sectional study examines burn incidence rates and accessibility of American Burn Association­verified or self-designated burn centers from 2013 to 2019.

Thoracic and Lumbar Spine Injury: Evidence-Based Diagnosis, Management, and Outcomes.

BACKGROUND: Traumatic thoracolumbar spine injuries are associated with significant morbidity and mortality. Targeted for non-spine specialist trauma surgeons, this systematic scoping review aimed to examine literature for up-to-date evidence on presentation, management, and outcomes of thoracolumbar spine injuries in adult trauma patients. METHODS: This review was reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. We searched four bibliographic databases: PubMed, EMBASE, Web of Science, and the Cochrane Library. Eligible studies included experimental, observational, and evidence-synthesis articles evaluating patients with thoracic, lumbar, or thoracolumbar spine injury, published in English between January 1, 2010 and January 31, 2021. Studies which focused on animals, cadavers, cohorts with N <30, and pediatric cohorts (age <18 years old), as well as case studies, abstracts, and commentaries were excluded. RESULTS: A total of 2501 studies were screened, of which 326 unique studies were fully text reviewed and twelve aspects of injury management were identified and discussed: injury patterns, determination of injury status and imaging options, considerations in management, and patient quality of life. We found: (1) imaging is a necessary diagnostic tool, (2) no consensus exists for preferred injury characterization scoring systems, (3) operative management should be considered for unstable fractures, decompression, and deformity, and (4) certain patients experience significant burden following injury. DISCUSSION: In this systematic scoping review, we present the most up-to-date information regarding the management of traumatic thoracolumbar spine injuries. This allows non-specialist trauma surgeons to become more familiar with thoracolumbar spine injuries in trauma patients and provides a framework for their management.

Development and Validation of a Model to Quantify Injury Severity in Real Time.

Importance: Quantifying injury severity is integral to trauma care benchmarking, decision-making, and research, yet the most prevalent metric to quantify injury severity-Injury Severity Score (ISS)- is impractical to use in real time. Objective: To develop and validate a practical model that uses a limited number of injury patterns to quantify injury severity in real time through 3 intuitive outcomes. Design, Setting, and Participants: In this cohort study for prediction model development and validation, training, development, and internal validation cohorts comprised 223 545, 74 514, and 74 514 admission encounters, respectively, of adults (age ≥18 years) with a primary diagnosis of traumatic injury hospitalized more than 2 days (2017-2018 National Inpatient Sample). The external validation cohort comprised 3855 adults admitted to a level I trauma center who met criteria for the 2 highest of the institution's 3 trauma activation levels. Main Outcomes and Measures: Three outcomes were hospital length of stay, probability of discharge disposition to a facility, and probability of inpatient mortality. The prediction performance metric for length of stay was mean absolute error. Prediction performance metrics for discharge disposition and inpatient mortality were average precision, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUROC). Calibration was evaluated using calibration plots. Shapley addictive explanations analysis and bee swarm plots facilitated model explainability analysis. Results: The Length of Stay, Disposition, Mortality (LDM) Injury Index (the model) comprised a multitask deep learning model trained, developed, and internally validated on a data set of 372 573 traumatic injury encounters (mean [SD] age = 68.7 [19.3] years, 56.6% female). The model used 176 potential injuries to output 3 interpretable outcomes: the predicted hospital length of stay, probability of discharge to a facility, and probability of inpatient mortality. For the external validation set, the ISS predicted length of stay with mean absolute error was 4.16 (95% CI, 4.13-4.20) days. Compared with the ISS, the model had comparable external validation set discrimination performance (facility discharge AUROC: 0.67 [95% CI, 0.67-0.68] vs 0.65 [95% CI, 0.65-0.66]; recall: 0.59 [95% CI, 0.58-0.61] vs 0.59 [95% CI, 0.58-0.60]; specificity: 0.66 [95% CI, 0.66-0.66] vs 0.62 [95%CI, 0.60-0.63]; mortality AUROC: 0.83 [95% CI, 0.81-0.84] vs 0.82 [95% CI, 0.82-0.82]; recall: 0.74 [95% CI, 0.72-0.77] vs 0.75 [95% CI, 0.75-0.76]; specificity: 0.81 [95% CI, 0.81-0.81] vs 0.76 [95% CI, 0.75-0.77]). The model had excellent calibration for predicting facility discharge disposition, but overestimated inpatient mortality. Explainability analysis found the inputs influencing model predictions matched intuition. Conclusions and Relevance: In this cohort study using a limited number of injury patterns, the model quantified injury severity using 3 intuitive outcomes. Further study is required to evaluate the model at scale.

Clinical prediction tool pitfalls and considerations: Data and algorithms.

In recent years, many surgical prediction models have been developed and published to augment surgeon decision-making, predict postoperative patient trajectories, and more. Collectively underlying all of these models is a wide variety of data sources and algorithms. Each data set and algorithm has its unique strengths, weaknesses, and type of prediction task for which it is best suited. The purpose of this piece is to highlight important characteristics of common data sources and algorithms used in surgical prediction model development so that future researchers interested in developing models of their own may be able to critically evaluate them and select the optimal ones for their study.

An Open-Source Curriculum to Teach Practical Academic Research Skills.

Academic productivity is important for career advancement, yet not all trainees have access to structured research programs. Without formal teaching, acquiring practical skills for research can be challenging. A comprehensive research course that teaches practical skills to translate ideas into publications could accelerate trainees' productivity and liberate faculty mentors' time. We share our experience designing and teaching "A Practical Introduction to Academic Research", a course that teaches practical skills including building productive habits, recognizing common statistical pitfalls, writing cover letters, succinct manuscripts, responding to reviewers, and delivering effective presentations. We share open-source educational material used during the Winter 2022 iteration to facilitate curriculum adoption at peer institutions.

TraumaICDBERT, A Natural Language Processing Algorithm to Extract Injury ICD-10 Diagnosis Code from Free Text.

OBJECTIVE: To develop and validate TraumaICDBERT, a natural language processing algorithm to predict injury ICD-10 diagnosis codes from trauma tertiary survey notes. SUMMARY BACKGROUND DATA: The adoption of ICD-10 diagnosis codes in clinical settings for injury prediction is hindered by the lack of real-time availability. Existing natural language processing algorithms have limitations in accurately predicting injury ICD-10 diagnosis codes. METHODS: Trauma tertiary survey notes from hospital encounters of adults between January 2016 and June 2021 were used to develop and validate TraumaICDBERT, an algorithm based on BioLinkBERT. The performance of TraumaICDBERT was compared to Amazon Web Services Comprehend Medical, an existing natural language processing tool. RESULTS: A dataset of 3,478 tertiary survey notes with 15,762 4-character injury ICD-10 diagnosis codes was analyzed. TraumaICDBERT outperformed Amazon Web Services Comprehend Medical across all evaluated metrics. On average, each tertiary survey note was associated with 3.8 (standard deviation: 2.9) trauma registrar-extracted 4-character injury ICD-10 diagnosis codes. CONCLUSIONS: TraumaICDBERT demonstrates promising initial performance in predicting injury ICD-10 diagnosis codes from trauma tertiary survey notes, potentially facilitating the adoption of downstream prediction tools in clinical settings.

For-Profit Status and Geographic Distribution of Trauma Centers in the US.

This cohort study assesses geographic distribution of for-profit and not-for-profit trauma centers in the US designated by their states between 2014 and 2018.

FasterRib: A deep learning algorithm to automate identification and characterization of rib fractures on chest computed tomography scans.

OBJECTIVE: Characterizing and enumerating rib fractures are critical to informing clinical decisions, yet in-depth characterization is rarely performed because of the manual burden of annotating these injuries on computed tomography (CT) scans. We hypothesized that our deep learning model, FasterRib , could predict the location and percentage displacement of rib fractures using chest CT scans. METHODS: The development and internal validation cohort comprised more than 4,700 annotated rib fractures from 500 chest CT scans within the public RibFrac. We trained a convolutional neural network to predict bounding boxes around each fracture per CT slice. Adapting an existing rib segmentation model, FasterRib outputs the three-dimensional locations of each fracture (rib number and laterality). A deterministic formula analyzed cortical contact between bone segments to compute percentage displacements. We externally validated our model on our institution's data set. RESULTS: FasterRib predicted precise rib fracture locations with 0.95 sensitivity, 0.90 precision, 0.92 f1 score, with an average of 1.3 false-positive fractures per scan. On external validation, FasterRib achieved 0.97 sensitivity, 0.96 precision, and 0.97 f1 score, and 2.24 false-positive fractures per scan. Our publicly available algorithm automatically outputs the location and percent displacement of each predicted rib fracture for multiple input CT scans. CONCLUSION: We built a deep learning algorithm that automates rib fracture detection and characterization using chest CT scans. FasterRib achieved the highest recall and the second highest precision among known algorithms in literature. Our open source code could facilitate FasterRib's adaptation for similar computer vision tasks and further improvements via large-scale external validation. LEVEL OF EVIDENCE: Diagnostic Tests/Criteria; Level III.

Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions.

OBJECTIVE: To summarize state-of-the-art artificial intelligence-enabled decision support in surgery and to quantify deficiencies in scientific rigor and reporting. BACKGROUND: To positively affect surgical care, decision-support models must exceed current reporting guideline requirements by performing external and real-time validation, enrolling adequate sample sizes, reporting model precision, assessing performance across vulnerable populations, and achieving clinical implementation; the degree to which published models meet these criteria is unknown. METHODS: Embase, PubMed, and MEDLINE databases were searched from their inception to September 21, 2022 for articles describing artificial intelligence-enabled decision support in surgery that uses preoperative or intraoperative data elements to predict complications within 90 days of surgery. Scientific rigor and reporting criteria were assessed and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. RESULTS: Sample size ranged from 163-2,882,526, with 8/36 articles (22.2%) featuring sample sizes of less than 2000; 7 of these 8 articles (87.5%) had below-average (<0.83) area under the receiver operating characteristic or accuracy. Overall, 29 articles (80.6%) performed internal validation only, 5 (13.8%) performed external validation, and 2 (5.6%) performed real-time validation. Twenty-three articles (63.9%) reported precision. No articles reported performance across sociodemographic categories. Thirteen articles (36.1%) presented a framework that could be used for clinical implementation; none assessed clinical implementation efficacy. CONCLUSIONS: Artificial intelligence-enabled decision support in surgery is limited by reliance on internal validation, small sample sizes that risk overfitting and sacrifice predictive performance, and failure to report confidence intervals, precision, equity analyses, and clinical implementation. Researchers should strive to improve scientific quality.

Appraising the Quality of Development and Reporting in Surgical Prediction Models.

This cross-sectional study uses the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis reporting guideline to assess 120 published studies about surgical prediction models.

Challenges in closing the gap between evidence and practice: International survey of institutional surgical stabilization of rib fractures guidelines.

BACKGROUND: Surgical stabilization of rib fractures (SSRF) has gained increasing interest over the past decade, yet few candidates who could benefit from SSRF undergo operative management. We conducted an international survey of institutional SSRF guidelines comparing congruence between practice and contemporary evidence. We hypothesized that few guidelines reflect comprehensive evidence to facilitate standardized patient selection, operation, and postoperative management. METHODS: A request for institutional rib fracture guidelines was distributed from the Chest Wall Injury Society. Surgical stabilization of rib fractures-specific guideline contents were extracted using a priori-designed extraction sheets and compared against 28 SSRF evidence-based recommendations outlined by a panel of 14 international experts. Fisher's exact test compared the proportion of strong and weak evidence-based recommendations specified within a majority of institutional guidelines to evaluate whether strength of evidence is associated with implementation. RESULTS: A total of 36 institutions from 3 countries submitted institutional rib fracture management guidelines, among which 30 had SSRF-specific guidance. Twenty-eight guidelines (93%) listed at least one injury pattern criteria as an indication for SSRF, while 22 (73%) listed pain and 21 (70%) listed impaired respiratory function as other indications. Quantitative pain and respiratory function impairment thresholds that warrant SSRF varied across institutions. Few guidelines specified nonacute indications for SSRF or perioperative considerations. Seven guidelines (23%) detailed postoperative management but recommended timing and interval for follow-up varied. Overall, only 3 of the 28 evidence-based SSRF recommendations were specified within a majority of institutional practice guidelines. There was no statistically significant association ( p = 0.99) between the strength of recommendation and implementation within institutional guidelines. CONCLUSION: Institutional SSRF guidelines do not reflect the totality of evidence available in contemporary literature. Guidelines are especially important for emerging interventions to ensure standardized care delivery and minimize low-value care. Consensus effort is needed to facilitate adoption and dissemination of evidence-based SSRF practices. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level IV.