Outcomes of Combined Opioids and Benzodiazepines Consumption in Elderly Trauma: A Retrospective Cohort Study.

BACKGROUND: Acute substance intoxication is associated with traumatic injury and worse hospital outcomes. The objective of this study was to evaluate the association between simultaneous opioids and benzodiazepines (OB) use and hospital outcomes in elderly trauma patients. METHODS: We performed a retrospective analysis using the American College of Surgeons Trauma Quality Improvement Program (ACS-TQIP) 2017 database. We included trauma patients (age ≥ 65 years) examined by urine toxicology within 24 hours of presentation. The primary outcome was in-hospital mortality. Secondary outcomes included hospital and ICU lengths of stay (HLOS AND ICULOS), in-hospital complications (eg, ventilator-associated pneumonia), unplanned intubation, and duration of mechanical ventilation. Patients were stratified being both positive for opioids and benzodiazepines (OB+) or not (OB-) based on having positive or negative drug screen for both drugs, respectively. A 1:1 propensity score matching was performed controlling for demographics (eg, age and sex), comorbidities (eg, alcoholism), and injury characteristics. RESULTS: Of 77,311 tested patients, 849 OB+ were matched to OB- patients. Compared to OB- group, OB+ patients were more likely to have unplanned intubation (26 [3.1%] vs 8 [0.9%], P = 0.002) and had prolonged HLOS (≥2 days: 683 [84.0%] vs 625 [77.8%], P = 0.002). There were no differences in all other outcomes (P > 0.05). CONCLUSIONS: The OB intake is associated with higher incidence of unplanned intubation and longer HLOS in elderly trauma patients. Early identification of elderly trauma patient with OB+ can help provide necessary pharmacologic and behavioral interventions to treat their substance use and potentially improve outcomes.

Development of a Machine Learning-Based Prescriptive Tool to Address Racial Disparities in Access to Care After Penetrating Trauma.

Importance: The use of artificial intelligence (AI) in clinical medicine risks perpetuating existing bias in care, such as disparities in access to postinjury rehabilitation services. Objective: To leverage a novel, interpretable AI-based technology to uncover racial disparities in access to postinjury rehabilitation care and create an AI-based prescriptive tool to address these disparities. Design, Setting, and Participants: This cohort study used data from the 2010-2016 American College of Surgeons Trauma Quality Improvement Program database for Black and White patients with a penetrating mechanism of injury. An interpretable AI methodology called optimal classification trees (OCTs) was applied in an 80:20 derivation/validation split to predict discharge disposition (home vs postacute care [PAC]). The interpretable nature of OCTs allowed for examination of the AI logic to identify racial disparities. A prescriptive mixed-integer optimization model using age, injury, and gender data was allowed to "fairness-flip" the recommended discharge destination for a subset of patients while minimizing the ratio of imbalance between Black and White patients. Three OCTs were developed to predict discharge disposition: the first 2 trees used unadjusted data (one without and one with the race variable), and the third tree used fairness-adjusted data. Main Outcomes and Measures: Disparities and the discriminative performance (C statistic) were compared among fairness-adjusted and unadjusted OCTs. Results: A total of 52 468 patients were included; the median (IQR) age was 29 (22-40) years, 46 189 patients (88.0%) were male, 31 470 (60.0%) were Black, and 20 998 (40.0%) were White. A total of 3800 Black patients (12.1%) were discharged to PAC, compared with 4504 White patients (21.5%; P < .001). Examining the AI logic uncovered significant disparities in PAC discharge destination access, with race playing the second most important role. The prescriptive fairness adjustment recommended flipping the discharge destination of 4.5% of the patients, with the performance of the adjusted model increasing from a C statistic of 0.79 to 0.87. After fairness adjustment, disparities disappeared, and a similar percentage of Black and White patients (15.8% vs 15.8%; P = .87) had a recommended discharge to PAC. Conclusions and Relevance: In this study, we developed an accurate, machine learning-based, fairness-adjusted model that can identify barriers to discharge to postacute care. Instead of accidentally encoding bias, interpretable AI methodologies are powerful tools to diagnose and remedy system-related bias in care, such as disparities in access to postinjury rehabilitation care.

Artificial intelligence versus surgeon gestalt in predicting risk of emergency general surgery.

BACKGROUND: Artificial intelligence (AI) risk prediction algorithms such as the smartphone-available Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) for emergency general surgery (EGS) are superior to traditional risk calculators because they account for complex nonlinear interactions between variables, but how they compare to surgeons' gestalt remains unknown. Herein, we sought to: (1) compare POTTER to surgeons' surgical risk estimation and (2) assess how POTTER influences surgeons' risk estimation. STUDY DESIGN: A total of 150 patients who underwent EGS at a large quaternary care center between May 2018 and May 2019 were prospectively followed up for 30-day postoperative outcomes (mortality, septic shock, ventilator dependence, bleeding requiring transfusion, pneumonia), and clinical cases were systematically created representing their initial presentation. POTTER's outcome predictions for each case were also recorded. Thirty acute care surgeons with diverse practice settings and levels of experience were then randomized into two groups: 15 surgeons (SURG) were asked to predict the outcomes without access to POTTER's predictions while the remaining 15 (SURG-POTTER) were asked to predict the same outcomes after interacting with POTTER. Comparing to actual patient outcomes, the area under the curve (AUC) methodology was used to assess the predictive performance of (1) POTTER versus SURG, and (2) SURG versus SURG-POTTER. RESULTS: POTTER outperformed SURG in predicting all outcomes (mortality-AUC: 0.880 vs. 0.841; ventilator dependence-AUC: 0.928 vs. 0.833; bleeding-AUC: 0.832 vs. 0.735; pneumonia-AUC: 0.837 vs. 0.753) except septic shock (AUC: 0.816 vs. 0.820). SURG-POTTER outperformed SURG in predicting mortality (AUC: 0.870 vs. 0.841), bleeding (AUC: 0.811 vs. 0.735), pneumonia (AUC: 0.803 vs. 0.753) but not septic shock (AUC: 0.712 vs. 0.820) or ventilator dependence (AUC: 0.834 vs. 0.833). CONCLUSION: The AI risk calculator POTTER outperformed surgeons' gestalt in predicting the postoperative mortality and outcomes of EGS patients, and when used, improved the individual surgeons' risk prediction. Artificial intelligence algorithms, such as POTTER, could prove useful as a bedside adjunct to surgeons when preoperatively counseling patients. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level II.

Is perioperative COVID-19 really associated with worse surgical outcomes? A nationwide COVIDSurg propensity-matched analysis.

BACKGROUND: Patients undergoing surgery with perioperative COVID-19 are suggested to have worse outcomes, but whether this is COVID-related or due to selection bias remains unclear. We aimed to compare the postoperative outcomes of patients with and without perioperative COVID-19. METHODS: Patients with perioperative COVID-19 diagnosed within 7 days before or 30 days after surgery between February and July 2020 from 68 US hospitals in COVIDSurg, an international multicenter database, were 1:1 propensity score matched to patients without COVID-19 undergoing similar procedures in the 2012 American College of Surgeons National Surgical Quality Improvement Program database. The matching criteria included demographics (e.g., age, sex), comorbidities (e.g., diabetes, chronic obstructive pulmonary disease, chronic kidney disease), and operation characteristics (e.g., type, urgency, complexity). The primary outcome was 30-day hospital mortality. Secondary outcomes included hospital length of stay and 13 postoperative complications (e.g., pneumonia, renal failure, surgical site infection). RESULTS: A total of 97,936 patients were included, 1,054 with and 96,882 without COVID-19. Prematching, COVID-19 patients more often underwent emergency surgery (76.1% vs. 10.3%, p < 0.001). A total of 843 COVID-19 and 843 non-COVID-19 patients were successfully matched based on demographics, comorbidities, and operative characteristics. Postmatching, COVID-19 patients had a higher mortality (12.0% vs. 8.1%, p = 0.007), longer length of stay (6 [2-15] vs. 5 [1-12] days), and higher rates of acute renal failure (19.3% vs. 3.0%, p < 0.001), sepsis (13.5% vs. 9.0%, p = 0.003), and septic shock (11.8% vs. 6.0%, p < 0.001). They also had higher rates of thromboembolic complications such as deep vein thrombosis (4.4% vs. 1.5%, p < 0.001) and pulmonary embolism (2.5% vs. 0.4%, p < 0.001) but lower rates of bleeding (11.6% vs. 26.1%, p < 0.001). CONCLUSION: Patients undergoing surgery with perioperative COVID-19 have higher rates of 30-day mortality and postoperative complications, especially thromboembolic, compared with similar patients without COVID-19 undergoing similar surgeries. Such information is crucial for the complex surgical decision making and counseling of these patients. LEVEL OF EVIDENCE: Prognostic and Epidemiologic; Level IV.

Enhanced Recovery After Surgery Patients Are Prescribed Fewer Opioids at Discharge: A Propensity-score Matched Analysis.

OBJECTIVE: We aimed to compare discharge opioid prescriptions pre- and post-ERAS implementation. SUMMARY OF BACKGROUND DATA: ERAS programs decrease inpatient opioid use, but their relationship with postdischarge opioids remains unclear. METHODS: All patients undergoing hysterectomy between October 2016 and November 2020 and pancreatectomy or hepatectomy between April 2017 and November 2020 at 1 tertiary care center were included. For each procedure, ERAS was implemented during the study period. PSM was performed to compare pre - versus post-ERAS patients on discharge opioids (number of pills and oral morphine equivalents). Patients were matched on age, sex, race, payor, American Society of Anesthesiologists score, prior opioid use, and procedure. Sensitivity analyses in open versus minimally invasive surgery cohorts were performed. RESULTS: A total of 3983 patients were included (1929 pre-ERAS; 2054 post-ERAS). Post-ERAS patients were younger (56.0 vs 58.4 years; P < 0.001), more often female (95.8% vs 78.1%; P < 0.001), less often white (77.2% vs 82.0%; P < 0.001), less often had prior opioid use (20.1% vs 28.1%; P < 0.001), and more often underwent hysterectomy (91.1% vs 55.7%; P < 0.001). After PSM, there were no significant differences between cohorts in baseline characteristics. Matched post-ERAS patients were prescribed fewer opioid pills (17.4 pills vs 22.0 pills; P < 0.001) and lower oral morphine equivalents (129.4 mg vs 167.6 mg; P < 0.001) than pre-ERAS patients. Sensitivity analyses confirmed these findings [open (18.8 pills vs 25.4 pills; P < 0.001 \ 138.9 mg vs 198.7 mg; P < 0.001); minimally invasive surgery (17.2 pills vs 21.1 pills; P < 0.001 \ 127.1 mg vs 160.1 mg; P < 0.001). CONCLUSIONS: Post-ERAS patients were prescribed significantly fewer opioids at discharge compared to matched pre-ERAS patients.

Mesenteric Ischemia in Patients with Coronavirus 2019: A Scoping Review.

Background: Coronavirus 2019 (COVID-19) is a systemic disease associated with severe gastrointestinal complications including life-threatening mesenteric ischemia. We sought to review and summarize the currently available literature on the presentation, management, and outcomes of mesenteric ischemia in patients with COVID-19. Patients and Methods: The PubMed database was searched to identify studies published between January 2020 and January 2021 that reported one or more adult (≥18 years) patients with COVID-19 who developed mesenteric ischemia during hospitalization. The demographic characteristics, clinical and imaging findings, management, and outcomes of patients from each study were extracted and summarized. Results: A total of 35 articles reporting on 61 patients with COVID-19 with mesenteric ischemia met the eligibility and were included in our study. The mean age was 60 (±15.9) years, and 53% of patients were male. Imaging findings of these patients included mesenteric arterial or venous thromboembolism, followed by signs of mesenteric ischemia. Sixty-seven percent of patients were taken to the operating room for an exploratory laparotomy and bowel resection and 21% were managed conservatively. The terminal ileum was the most commonly involved area of necrosis (26%). The mortality rate of patients with COVID-19 with mesenteric ischemia was 33%, and the most common cause of death was multiorgan failure or refractory septic shock. Twenty-seven percent of patients managed operatively died during the post-operative period. Conclusions: Mesenteric ischemia in patients with COVID-19 is a devastating complication associated with a high rate of morbidity and mortality. Further efforts should focus on developing strategies for early recognition and management.

The variation of opioid prescription after injury and its association with long-term chronic pain: A multicenter cohort study.

BACKGROUND: Opioid overprescription in trauma contributes to the opioid epidemic through diversion of unused pills. Through our study, we sought to do the following: (1) understand the variation in opioid prescription after injury and its relationship to patient and/or clinical variables, and (2) study the relationship between opioid prescribing and long-term pain and analgesic use. METHOD: Trauma patients with an injury severity score ≥9 admitted to 3 level 1 trauma centers were screened for chronic pain and analgesic use 6 to 12 months postinjury. First, multivariable linear regression models were constructed with "oral morphine equivalents" and "number of opioid pills prescribed" at discharge as dependent variables. The coefficients of determination were calculated to determine how much of the variation in opioid prescription was explained by patient and clinical variables. Second, a multivariable logistic regression analysis was created to study the association between opioid prescription at discharge and chronic pain/analgesic use at 6 to 12 months. Analyses were adjusted for patient demographics, socioeconomics, comorbidities, injury parameters, and hospital course. RESULTS: Of the 2,702 patients included (mean [standard deviation] age: 61.0 [21.5]; 55% males), 74% were prescribed opioids at discharge (mean number of pills [standard deviation]: 24.0 [26.5]; mean oral morphine equivalent [standard deviation]: 204.8 [348.1]). The adjusted coefficients of determination for oral morphine equivalents and number of pills was 0.12 and 0.21, respectively, suggesting that the measured patient and clinical factors explain <21% of the variation in opioid prescribing in trauma. Patients prescribed opioids were more likely to have chronic pain (odds ratio [95%] confidence interval: 1.34 [1.05-1.71]) and use analgesics daily (odds ratio [95%] confidence interval: 1.86 [1.25-2.77]) 6 to 12 months postinjury. CONCLUSION: The variation in opioid prescription after traumatic injury is more affected by system and provider level rather than clinical or patient-related factors, and opioid prescribing correlates independently with long-term chronic pain and continued analgesic use postinjury. Efforts to decrease opioid use should prioritize standardizing prescription practices after traumatic injury.

Critically Appraising the Quality of Reporting of American College of Surgeons TQIP Studies in the Era of Large Data Research.

BACKGROUND: The American College of Surgeons-Trauma Quality Improvement Program (ACS-TQIP) database is one of the most widely used databases for trauma research. We aimed to critically appraise the quality of the methodological reporting of ACS-TQIP studies. STUDY DESIGN: The ACS-TQIP bibliography was queried for all studies published between January 2018 and January 2021. The quality of data reporting was assessed using the Strengthening the Reporting of Observational studies in Epidemiology-Reporting of Studies Conducted Using Observational Routinely Collected Health Data (STROBE-RECORD) statement and the JAMA Surgery checklist. Three items from each tool were not applicable and thus excluded. The quality of reporting was compared between high- and low-impact factor (IF) journals (cutoff for high IF is >90th percentile of all surgical journals). RESULTS: A total of 118 eligible studies were included; 12 (10%) were published in high-IF journals. The median (interquartile range) number of criteria fulfilled was 5 (4-6) for the STROBE-RECORD statement (of 10 items) and 5 (5-6) for the JAMA Surgery checklist (of 7 items). Specifically, 73% of studies did not describe the patient population selection process, 61% did not address data cleaning or the implications of missing values, and 76% did not properly state inclusion/exclusion criteria and/or outcome variables. Studies published in high-IF journals had remarkably higher quality of reporting than those in low-IF journals. CONCLUSION: The methodological reporting quality of ACS-TQIP studies remains suboptimal. Future efforts should focus on improving adherence to standard reporting guidelines to mitigate potential bias and improve the reproducibility of published studies.

POTTER-ICU: An artificial intelligence smartphone-accessible tool to predict the need for intensive care after emergency surgery.

BACKGROUND: Delays in admitting high-risk emergency surgery patients to the intensive care unit result in worse outcomes and increased health care costs. We aimed to use interpretable artificial intelligence technology to create a preoperative predictor for postoperative intensive care unit need in emergency surgery patients. METHODS: A novel, interpretable artificial intelligence technology called optimal classification trees was leveraged in an 80:20 train:test split of adult emergency surgery patients in the 2007-2017 American College of Surgeons National Surgical Quality Improvement Program database. Demographics, comorbidities, and laboratory values were used to develop, train, and then validate optimal classification tree algorithms to predict the need for postoperative intensive care unit admission. The latter was defined as postoperative death or the development of 1 or more postoperative complications warranting critical care (eg, unplanned intubation, ventilator requirement ≥48 hours, cardiac arrest requiring cardiopulmonary resuscitation, and septic shock). An interactive and user-friendly application was created. C statistics were used to measure performance. RESULTS: A total of 464,861 patients were included. The mean age was 55 years, 48% were male, and 11% developed severe postoperative complications warranting critical care. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application was created as the user-friendly interface of the complex optimal classification tree algorithms. The number of questions (ie, tree depths) needed to predict intensive care unit admission ranged from 2 to 11. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application had excellent discrimination for predicting the need for intensive care unit admission (C statistics: 0.89 train, 0.88 test). CONCLUSION: We recommend the Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application as an accurate, artificial intelligence-based tool for predicting severe complications warranting intensive care unit admission after emergency surgery. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application can prove useful to triage patients to the intensive care unit and to potentially decrease failure to rescue in emergency surgery patients.

Perceived Socioeconomic Status: A Strong Predictor of Long-Term Outcomes After Injury.

BACKGROUND: Socioeconomic status (SES) is defined as a total measure of an individual's economic or social position in relation to others. Income and educational level are often used as quantifiable objective measures of SES but are inherently limited. Perceived SES (p-SES), refers to an individual's perception of their own SES. Herein, we assess the correlation between objective SES (o-SES) as defined by income and educational level and p-SES after injury and compare their associations with long-term outcomes after injury. METHODS: Moderate-to-severely injured patients admitted to a Level 1 trauma center were asked to complete a phone-based survey assessing functional and mental health outcomes, social dysfunction, chronic pain, and return to work/school 6-12 mo postinjury. o-SES was determined by income and educational level (low educational level: high school or lower; low income: live in zip code with median income/household lower than the national median). p-SES was determined by asking patients to categorize their SES. The correlation coefficient between o-SES and p-SES was calculated. Multivariate logistic regression models were built to determine the associations between o-SES and p-SES and long-term outcomes. RESULTS: A total of 729 patients were included in this study. Patients who reported a low p-SES were younger, more likely to suffer penetrating injuries, and to have a weak social support network. Twenty-one percent of patients with high income and high educational level classified their p-SES as low or mid-low, and conversely, 46% of patients with low education and low income classified their p-SES as high or mid-high. The correlation coefficient between p-SES and o-SES was 0.2513. After adjusting for confounders, p-SES was a stronger predictor of long-term outcomes, including functional limitations, social dysfunction, mental health outcomes, return to work/school, and chronic pain than was o-SES. CONCLUSIONS: Patient-reported p-SES correlates poorly with o-SES indicating that the commonly used calculation of income and education may not accurately capture an individuals' SES. Furthermore, we found p-SES to be more strongly correlated with long-term outcome measures than o-SES. As we strive to improve long-term outcomes after injury, p-SES may be an important variable in the early identification of individuals who are likely to suffer from worse long-term outcomes after injury.

The Beirut Port Explosion Injuries and Lessons Learned: Results of the Beirut Blast Assessment for Surgical Services (BASS) Multicenter Study.

OBJECTIVE: This multicenter study aims to describe the injury patterns, emergency management and outcomes of the blast victims, recognize the gaps in hospital disaster preparedness, and identify lessons to be learned. SUMMARY BACKGROUND DATA: On August 4th, 2020, the city of Beirut, Lebanon suffered the largest urban explosion since Hiroshima and Nagasaki, resulting in hundreds of deaths and thousands of injuries. METHODS: All injured patients admitted to four of the largest Beirut hospitals within 72 hours of the blast, including those who died on arrival or in the emergency department (ED), were included. Medical records were systematically reviewed for: patient demographics and comorbidities; injury severity and characteristics; prehospital, ED, operative, and inpatient interventions; and outcomes at hospital discharge. Lessons learned are also shared. RESULTS: An estimated total of 1818 patients were included, of which 30 died on arrival or in the ED and 315 were admitted to the hospital. Among admitted patients, the mean age was 44.7 years (range: 1 week-93 years), 44.4% were female, and the median injury severity score (ISS) was 10 (5, 17). ISS was inversely related to the distance from the blast epicenter (r = --0.18, P = 0.035). Most injuries involved the upper extremities (53.7%), face (42.2%), and head (40.3%). Mildly injured (ISS <9) patients overwhelmed the ED in the first 2 hours; from hour 2 to hour 8 post-injury, the number of moderately, severely, and profoundly injured patients increased by 127%, 25% and 17%, respectively. A total of 475 operative procedures were performed in 239 patients, most commonly soft tissue debridement or repair (119 patients, 49.8%), limb fracture fixation (107, 44.8%), and tendon repair (56, 23.4%). A total of 11 patients (3.5%) died during the hospitalization, 56 (17.8%) developed at least 1 complication, and 51 (16.2%) were discharged with documented long-term disability. Main lessons learned included: the importance of having key hospital functions (eg, laboratory, operating room) underground; the nonadaptability of electronic medical records to disasters; the ED overwhelming with mild injuries, delay in arrival of the severely injured; and the need for realistic disaster drills. CONCLUSIONS: We, therefore, describe the injury patterns, emergency flow and trauma outcome of patients injured in the Beirut port explosion. The clinical and system-level lessons learned can help prepare for the next disaster.

Validation of the artificial intelligence-based trauma outcomes predictor (TOP) in patients 65 years and older.

BACKGROUND: The Trauma Outcomes Predictor tool was recently derived using a machine learning methodology called optimal classification trees and validated for prediction of outcomes in trauma patients. The Trauma Outcomes Predictor is available as an interactive smartphone application. In this study, we sought to assess the performance of the Trauma Outcomes Predictor in the elderly trauma patient. METHODS: All patients aged 65 years and older in the American College of Surgeons-Trauma Quality Improvement Program 2017 database were included. The performance of the Trauma Outcomes Predictor in predicting in-hospital mortality and combined and specific morbidity based on incidence of 9 specific in-hospital complications was assessed using the c-statistic methodology, with planned subanalyses for patients 65 to 74, 75 to 84, and 85+ years. RESULTS: A total of 260,505 patients were included. Median age was 77 (71-84) years, 57% were women, and 98.8% had a blunt mechanism of injury. The Trauma Outcomes Predictor accurately predicted mortality in all patients, with excellent performance for penetrating trauma (c-statistic: 0.92) and good performance for blunt trauma (c-statistic: 0.83). Its best performance was in patients 65 to 74 years (c-statistic: blunt 0.86, penetrating 0.93). Among blunt trauma patients, the Trauma Outcomes Predictor had the best discrimination for predicting acute respiratory distress syndrome (c-statistic 0.75) and cardiac arrest requiring cardiopulmonary resuscitation (c-statistic 0.75). Among penetrating trauma patients, the Trauma Outcomes Predictor had the best discrimination for deep and organ space surgical site infections (c-statistics 0.95 and 0.84, respectively). CONCLUSION: The Trauma Outcomes Predictor is a novel, interpretable, and highly accurate predictor of in-hospital mortality in the elderly trauma patient up to age 85 years. The Trauma Outcomes Predictor could prove useful for bedside counseling of elderly patients and their families and for benchmarking the quality of geriatric trauma care.

Patient-reported outcomes 6 to 12 months after isolated rib fractures: A nontrivial injury pattern.

BACKGROUND: Despite the ubiquity of rib fractures in patients with blunt chest trauma, long-term outcomes for patients with this injury pattern are not well described. METHODS: The Functional Outcomes and Recovery after Trauma Emergencies (FORTE) project has established a multicenter prospective registry with 6- to 12-month follow-up for trauma patients treated at participating centers. We combined the FORTE registry with a detailed retrospective chart review investigating admission variables and injury characteristics. All trauma survivors with complete FORTE data and isolated chest trauma (Abbreviated Injury Scale score of ≤1 in all other regions) with rib fractures were included. Outcomes included chronic pain, limitation in activities of daily living, physical limitations, exercise limitations, return to work, and both inpatient and discharge pain control modalities. Multivariable logistic regression models were built for each outcome using clinically relevant demographic and injury characteristic univariate predictors. RESULTS: We identified 279 patients with isolated rib fractures. The median age of the cohort was 68 years (interquartile range, 56-78 years), 59% were male, and 84% were White. Functional and quality of life limitations were common among survivors of isolated rib fractures even 6 to 12 months after injury. Forty-three percent of patients without a preexisting pain disorder reported new daily pain, and new chronic pain was associated with low resilience. Limitations in physical functioning and exercise capacity were reported in 56% and 51% of patients, respectively. Of those working preinjury, 28% had not returned to work. New limitations in activities of daily living were reported in 29% of patients older than 65 years. Older age, higher number of rib fractures, and intensive care unit admission were independently associated with higher odds of receiving regional anesthesia. Receiving a regional nerve block did not have a statistically significant association with any patient-reported outcome measures. CONCLUSION: Isolated rib fractures are a nontrivial trauma burden associated with functional impairment and chronic pain even 6 to 12 months after injury. LEVEL OF EVIDENCE: Prognostic/epidemiologic, level III.

Incidence and Predictors of Surgical Site Infection Complications in Diabetic Patients Undergoing Lower Limb Amputation.

BACKGROUND: Surgical site infections (SSIs) following lower extremity amputations (LEAs) are a major cause of patient morbidity and mortality. The objectives of this study are to investigate the annual incidence of SSI and risk factors associated with SSI after LEA in diabetic patients. METHODS: LEAs performed on diabetic patients between 2005 and 2017 were retrospectively analyzed from the American College of Surgeons National Surgical Quality Improvement Program database. Incidence rates were calculated and analyzed for temporal change. Multivariable logistic regression was conducted to identify the independent predictors of SSIs in LEA. RESULTS: In 21,449 diabetic patients, the incidence of SSIs was 6.8% after LEA, with an overall decreasing annual trend (P = 0.013). Amputation location (below-knee in reference to above-knee) [OR (95% CI): 1.35 (1.20 - 1.53), P <0.001], smoking [OR (95% CI): 1.25 (1.11 - 1.41), P <0.001)], female sex [OR (95% CI): 1.16 (1.03 - 1.30)], preoperative sepsis [OR (95% CI): 1.24 (1.10 - 1.40), P <0.001], P = 0.013], emergency status [OR (95% CI): 1.38 (1.17 - 1.63), P <0.001], and obesity [OR (95% CI): 1.59 (1.12 - 2.27), P = 0.009] emerged as independent predictors of SSIs, while moderate/severe anemia emerged as a risk-adjusted protective factor [OR (95% CI): 0.75 (0.62 - 0.91), P = 0.003]. Sensitivity analysis found that moderate/severe anemia, not body mass index (BMI) class, remained a significant risk factor in the development of SSIs in below-the-knee amputations; in contrast, higher BMI, not preoperative hematocrit, was significantly associated with an increased risk for SSI in above-the-knee amputations. CONCLUSIONS: The incidence of SSIs after LEA in diabetic patients is decreasing. Overall, below-knee amputation, smoking, emergency status, and preoperative sepsis appeared to be associated with SSIs. Obesity increased SSIs in above-the-knee amputations, while moderate/severe preoperative anemia appears to protect against below-the-knee SSIs. Surgeons should take predictors of SSI into consideration while optimizing care for their patients, and future studies should investigate the role of preoperative hematocrit correction and how it may influence outcomes positively or negatively.

The surgical technique of the convergent procedure.

The convergent procedure is a newly developed hybrid ablation procedure that involves extensive epicardial ablation of the posterior left atrial wall followed by endocardial mapping and addition of pulmonary vein isolation. It is a team-based approach that provides a promising option for patients with persistent and permanent atrial fibrillation. In this manuscript, we present a detailed description of the surgical component of this procedure and include potential pitfalls based on our experience in performing it.

Impact of the COVID-19 Pandemic on Long-term Recovery From Traumatic Injury.

OBJECTIVE: Determine the proportion and characteristics of traumatic injury survivors who perceive a negative impact of the COVID-19 pandemic on their recovery and to define post-injury outcomes for this cohort. BACKGROUND: The COVID-19 pandemic has precipitated physical, psychological, and social stressors that may create a uniquely difficult recovery and reintegration environment for injured patients. METHODS: Adult (≥18 years) survivors of moderate-to-severe injury completed a survey 6 to 14 months post-injury during the COVID-19 pandemic. This survey queried individuals about the perceived impact of the COVID-19 pandemic on injury recovery and assessed post-injury functional and mental health outcomes. Regression models were built to identify factors associated with a perceived negative impact of the pandemic on injury recovery, and to define the relationship between these perceptions and long-term outcomes. RESULTS: Of 597 eligible trauma survivors who were contacted, 403 (67.5%) completed the survey. Twenty-nine percent reported that the COVID-19 pandemic negatively impacted their recovery and 24% reported difficulty accessing needed healthcare. Younger age, lower perceived-socioeconomic status, extremity injury, and prior psychiatric illness were independently associated with negative perceived impact of the COVID-19 pandemic on injury recovery. In adjusted analyses, patients who reported a negative impact of the pandemic on their recovery were more likely to have new functional limitations, daily pain, lower physical and mental component scores of the Short-Form-12 and to screen positive for PTSD and depression. CONCLUSIONS: The COVID-19 pandemic is negatively impacting the recovery of trauma survivors. It is essential that we recognize the impact of the pandemic on injured patients while focusing on directed efforts to improve the long-term outcomes of this already at-risk population.

Long-Term Functional Outcomes of Trauma Patients With Facial Injuries.

BACKGROUND: Facial trauma can have long-lasting consequences on an individual's physical, mental, and social well-being. The authors sought to assess the long-term outcomes of patients with facial injuries. METHODS: This is a prospective multicenter cohort study of patients with face abbreviated injury scores ≥1 within the Functional Outcomes and Recovery after Trauma Emergencies registry. The Functional Outcomes and Recovery after Trauma Emergencies registry collects patient-reported outcomes data for patients with moderate-severe trauma 6 to 12 months after injury. Outcomes variables included general and trauma-specific quality of life, functional limitations, screening for post-traumatic stress disorder, and postdischarge healthcare utilization. RESULTS: A total of 188 patients with facial trauma were included: 69.1% had an isolated face and/or head injury and 30.9% had a face and/or head injuries as a part of polytrauma injury. After discharge, 11.7% of patients visited the emergency room, and 13.3% were re-admitted to the hospital. Additionally, 36% of patients suffered from functional limitations and 17% of patients developed post-traumatic stress disorder. A total of 34.3% patients reported that their injury scars bothered them, and 49.4% reported that their injuries were hard to deal with emotionally. CONCLUSIONS: Patients who sustain facial trauma suffer significant long-term health-related quality of life consequences stemming from their injuries.