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Prior studies assessing the effects of Impella 5.5 support duration on posttransplant outcomes have been limited to single-center case reports and series. This study evaluates the impact of Impella 5.5 support duration on outcomes following heart transplantation using the United Network for Organ Sharing database. Adult heart transplant recipients who were directly bridged to primary isolated heart transplantation with Impella 5.5 were included. The cohort was stratified into two groups based on the duration of Impella support: less than or equal to 14 and greater than 14 days. The primary outcome was 90 day posttransplant survival. Propensity score matching was performed. Sub-analysis was conducted to evaluate the impact of greater than 30 days of Impella support on 90 day survival. Three hundred thirty-two recipients were analyzed. Of these, 212 recipients (63.9%) were directly bridged to heart transplantation with an Impella support duration of greater than 14 days. The two groups had comparable 90 day posttransplant survival and complication rates. The comparable posttransplant survival persisted in a propensity score-matched comparison. In the sub-analysis, Impella support duration of greater than or equal to 30 days did not adversely impact 90 day survival. This study demonstrates that extended duration of support with Impella 5.5 as a bridge to transplantation does not adversely impact posttransplant outcomes. Impella 5.5 is a safe and effective bridging modality to heart transplantation.
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BACKGROUND: This study evaluates the clinical trends, risk factors, and impact of waitlist blood transfusion on outcomes following isolated heart transplantation. METHODS: The UNOS registry was queried to identify adult recipients from January 1, 2014, to June 30, 2022. The recipients were stratified into two groups depending on whether they received a blood transfusion while on the waitlist. The incidence of waitlist transfusion was compared before and after the 2018 allocation policy change. The primary outcome was survival. Propensity score-matching was performed. Multivariable logistic regression was performed to identify predictors of waitlist transfusion. A sub-analysis was performed to evaluate the impact of waitlist time on waitlist transfusion. RESULTS: From the 21 926 recipients analyzed in this study, 4201 (19.2%) received waitlist transfusion. The incidence of waitlist transfusion was lower following the allocation policy change (14.3% vs. 23.7%, p < 0.001). The recipients with waitlist transfusion had significantly reduced 1-year posttransplant survival (88.8% vs. 91.9%, p < 0.001) compared to the recipients without waitlist transfusion in an unmatched comparison. However, in a propensity score-matched comparison, the two groups had similar 1-year survival (90.0% vs. 90.4%, p = 0.656). Multivariable analysis identified ECMO, Impella, and pretransplant dialysis as strong predictors of waitlist transfusion. In a sub-analysis, the odds of waitlist transfusion increased nonlinearly with longer waitlist time. CONCLUSION: There is a lower incidence of waitlist transfusion among transplant recipients under the 2018 allocation system. Waitlist transfusion is not an independent predictor of adverse posttransplant outcomes but rather a marker of the patient's clinical condition. ECMO, Impella, and pretransplant dialysis are strong predictors of waitlist transfusion.
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Transfusão de Sangue , Transplante de Coração , Sistema de Registros , Listas de Espera , Humanos , Masculino , Listas de Espera/mortalidade , Feminino , Transplante de Coração/efeitos adversos , Transplante de Coração/mortalidade , Pessoa de Meia-Idade , Seguimentos , Fatores de Risco , Prognóstico , Taxa de Sobrevida , Transfusão de Sangue/estatística & dados numéricos , Sobrevivência de Enxerto , Adulto , Estudos RetrospectivosRESUMO
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.
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OBJECTIVE: Evaluate the interaction between whole blood (WB) and blood component resuscitation in relation to mortality following trauma. SUMMARY BACKGROUND DATA: WB is increasingly available in civilian trauma resuscitation, and it is typically transfused concomitantly with blood components. The interaction between WB and blood component transfusions is unclear. METHODS: Adult trauma patients with a shock index >1 who received ≥4 combined units of red blood cells (RBC) or WB within 4 hours across 501 United States trauma centers were included using the American College of Surgeons Trauma Quality Improvement Program (ACS-TQIP) database. The associations between 1)WB resuscitation and mortality, 2)WB to total transfusion volume ratio (WB:TTV) and mortality, 3)balanced blood component transfusion in the setting of combined WB and component resuscitation and mortality were evaluated with multivariable analysis. RESULTS: A total of 12,275 patients were included (WB: 2,884 vs. component-only: 9,391). WB resuscitation was associated with lower odds of 4-hour (adjusted odds ratio [aOR]: 0.81 [0.68-0.97]), 24-hour, and 30-day mortality compared to component-only. Higher WB:TTV ratios were significantly associated with lower 4-hour, 24-hour, and 30-day mortality, with a 13% decrease in odds of 4-hour mortality for each 10% increase in the WB:TTV ratio (0.87 [95%CI:0.80 - 0.94]). Balanced blood component transfusion was associated with significantly lower odds of 4-hour (aOR: 0.45 [95%CI: 0.29 - 0.68]), 24-hour, and 30-day mortality in the setting of combined WB and blood component resuscitation. CONCLUSIONS: WB resuscitation, higher WB:TTV ratios, and balanced blood component transfusion in conjunction with WB were associated with lower mortality in trauma patients presenting in shock requiring 4 units of RBC and/or WB transfusion within 4 hours of arrival.
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BACKGROUND: The diversion of unused opioid prescription pills to the community at large contributes to the opioid epidemic in the US. In this county-level population-based study, we aimed to examine the US surgeons' opioid prescription patterns, trends, and system-level predictors in the peak years of the opioid epidemic. STUDY DESIGN: Using the Medicare Part D database (2013 to 2017), the mean number of opioid prescriptions per beneficiary (OPBs) was determined for each US county. Opioid-prescribing patterns were compared across counties. Multivariable linear regression was performed to determine relationships between county-level social determinants of health (demographic, eg median age and education level; socioeconomic, eg median income; population health status, eg percentage of current smokers; healthcare quality, eg rate of preventable hospital stays; and healthcare access, eg healthcare costs) and OPBs. RESULTS: Opioid prescription data were available for 1,969 of 3,006 (65.5%) US counties, and opioid-related deaths were recorded in 1,384 of 3,006 counties (46%). Nationwide, the mean OPBs decreased from 1.08 ± 0.61 in 2013 to 0.87 ± 0.55 in 2017; 81.6% of the counties showed the decreasing trend. County-level multivariable analyses showed that lower median population age, higher percentages of bachelor's degree holders, higher percentages of adults reporting insufficient sleep, higher healthcare costs, fewer mental health providers, and higher percentages of uninsured adults are associated with higher OPBs. CONCLUSIONS: Opioid prescribing by surgeons decreased between 2013 and 2017. A county's suboptimal access to healthcare in general and mental health services in specific may be associated with more opioid prescribing after surgery.
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Acessibilidade aos Serviços de Saúde , Medicare Part D , Serviços de Saúde Mental , Determinantes Sociais da Saúde , Adulto , Idoso , Humanos , Analgésicos Opioides/uso terapêutico , Padrões de Prática Médica , Estados Unidos , Procedimentos Cirúrgicos OperatóriosRESUMO
BACKGROUND: A novel hydrophobically modified chitosan (hm-chitosan) polymer has been previously shown to improve survival in a non-compressible intra-abdominal bleeding model in swine. We performed a 28-day survival study to evaluate the safety of the hm-chitosan polymer in swine. METHODS: Female Yorkshire swine (40-50 kg) were used. A mild, non-compressible, closed-cavity bleeding model was created with splenic transection. The hm-chitosan polymer was applied intra-abdominally through an umbilical nozzle in the same composition and dose previously shown to improve survival. Animals were monitored intraoperatively and followed 28 days postoperatively for survival, signs of pain, and end-organ function. Gross pathological and microscopic evaluations were performed at the conclusion of the experiment. RESULTS: A total of 10 animals were included (hm-chitosan = 8; control = 2). The 2 control animals survived through 28 days, and 7 of the 8 animals from the hm-chitosan group survived without any adverse events. One animal from the hm-chitosan group required early termination of the study for signs of pain, and superficial colonic ulcers were found on autopsy. Laboratory tests showed no signs of end-organ dysfunction after exposure to hm-chitosan after 28 days. On gross pathological examination, small (<0.5 cm) peritoneal nodules were noticed in the hm-chitosan group, which were consistent with giant-cell foreign body reaction in microscopy, presumably related to polymer remnants. Microscopically, no signs of systemic polymer embolization or thrombosis were noticed. CONCLUSION: Prolonged intraperitoneal exposure to the hm-chitosan polymer was tolerated without any adverse event in the majority of animals. In the single animal that required early termination, the material did not appear to be associated with end-organ dysfunction in swine. Superficial colonic ulcers that would require surgical repair were identified in 1 out of 8 animals exposed to hm-chitosan.
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Quitosana , Feminino , Animais , Suínos , Quitosana/efeitos adversos , Insuficiência de Múltiplos Órgãos , Úlcera , Hemorragia/etiologia , Hemorragia/terapia , Biopolímeros , DorRESUMO
BACKGROUND: While cryoprecipitate (Cryo) is commonly included in massive transfusion protocols for hemorrhagic shock, the optimal dose of Cryo transfusion remains unknown. We evaluated the optimal red blood cell (RBC) to RBC to Cryo ratio during resuscitation in massively transfused trauma patients. METHODS: Adult patients in the American College of Surgeon Trauma Quality Improvement Program (2013-2019) receiving massive transfusion (≥4 U of RBCs, ≥1 U of fresh frozen plasma, and ≥1 U of platelets within 4 hours) were included. A unit of Cryo was defined as a pooled unit of 100 mL. The RBC:Cryo ratio was calculated for blood products transfused within 4 hours of presentation. The association between RBC:Cryo and 24-hour mortality was analyzed with multivariable logistic regression adjusting for the volume of RBC, plasma and platelet transfusions, global and regional injury severity, and other relevant variables. RESULTS: The study cohort included 12,916 patients. Among those who received Cryo (n = 5,511 [42.7%]), the median RBC and Cryo transfusion volume within 4 hours was 11 U (interquartile range, 7-19 U) and 2 U (interquartile range, 1-3 U), respectively. Compared with no Cryo administration, only RBC:Cryo ratios ≤8:1 were associated with a significant survival benefit, while lower doses of Cryo (RBC:Cryo >8:1) were not associated with decreased 24-hour mortality. Compared with the maximum dose of Cryo administration (RBC:Cryo, 1:1-2:1), there was no difference in 24-hour mortality up to RBC:Cryo of 7:1 to 8:1, whereas lower doses of Cryo (RBC:Cryo, >8:1) were associated with significantly increased 24-hour mortality. CONCLUSION: One pooled unit of Cryo (100 mL) per 7 to 8 U of RBCs could be the optimal dose of Cryo in trauma resuscitation that provides a significant survival benefit while avoiding unnecessary blood product transfusions. LEVEL OF EVIDENCE: Prognostic and Epidemiologic; Level IV.
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Transfusão de Eritrócitos , Ferimentos e Lesões , Adulto , Humanos , Transfusão de Eritrócitos/métodos , Estudos Retrospectivos , Transfusão de Sangue , Transfusão de Plaquetas/métodos , Plasma , Ferimentos e Lesões/complicações , Ferimentos e Lesões/terapia , Centros de TraumatologiaRESUMO
BACKGROUND: Existent methodologies for benchmarking the quality of surgical care are linear and fail to capture the complex interactions of preoperative variables. We sought to leverage novel nonlinear artificial intelligence methodologies to benchmark emergency surgical care. METHODS: Using a nonlinear but interpretable artificial intelligence methodology called optimal classification trees, first, the overall observed mortality rate at the index hospital's emergency surgery population (index cohort) was compared to the risk-adjusted expected mortality rate calculated by the optimal classification trees from the American College of Surgeons National Surgical Quality Improvement Program database (benchmark cohort). Second, the artificial intelligence optimal classification trees created different "nodes" of care representing specific patient phenotypes defined by the artificial intelligence optimal classification trees without human interference to optimize prediction. These nodes capture multiple iterative risk-adjusted comparisons, permitting the identification of specific areas of excellence and areas for improvement. RESULTS: The index and benchmark cohorts included 1,600 and 637,086 patients, respectively. The observed and risk-adjusted expected mortality rates of the index cohort calculated by optimal classification trees were similar (8.06% [95% confidence interval: 6.8-9.5] vs 7.53%, respectively, P = .42). Two areas of excellence and 4 for improvement were identified. For example, the index cohort had lower-than-expected mortality when patients were older than 75 and in respiratory failure and septic shock preoperatively but higher-than-expected mortality when patients had respiratory failure preoperatively and were thrombocytopenic, with an international normalized ratio ≤1.7. CONCLUSION: We used artificial intelligence methodology to benchmark the quality of emergency surgical care. Such nonlinear and interpretable methods promise a more comprehensive evaluation and a deeper dive into areas of excellence versus suboptimal care.
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Serviços Médicos de Emergência , Insuficiência Respiratória , Humanos , Inteligência Artificial , Benchmarking , Bases de Dados FactuaisRESUMO
Background: Rib fractures are the most common traumatic injury. Hemothorax is one of the widespread complications associated with a rib fracture and occurs in 10-37 % of all rib fractures. Delayed hemothorax (DHTX) is defined as an accumulation of blood within the pleural cavity. Although there is extensive literature on hemothorax, there is limited literature on rib fractures and DHTX readmissions. The objective of this study was to identify potential risk factors for DHTX readmission and examine descriptive information on readmission. Methods: Using the 2016-2019 National Readmission Database (NRD), patients that experienced an admission with a blunt traumatic rib fracture were included. It was determined if the patients experienced DHTX by screening for an admission containing an ICD-10 code for hemothorax within 30 days after an admission containing a ICD-10 code for rib fracture. Univariable and multivariable analysis was performed to determine independent risk factors associated with DHTX readmission. Additionally, information on the clinical and financial characteristics of DHTX readmissions were examined. Results: A total of 242,071 patients were included, of whom 635 experienced DHTX readmission ≤30 days after discharge. Diagnosed with hemothorax on the index admission had the largest odds ratio for DHTX readmission (7.43 [6.14-8.99], P < 0.001). Complications found during DHTX readmission included acute respiratory failure (16.9 %), sepsis (6.9 %), and empyema (4.3 %). Treatment mainly consisted of pleural drainage (62.2 %) and video-assisted thoracoscopic surgery (VATS) evacuation of hemothorax (10.1 %). Conclusion: Patients admitted for a rib fracture have a low incidence for DHTX readmission within 30 days. However, multivariable analysis has demonstrated some risk and protective factors associated with DHTX readmission. Further studies should focus on exploring these risk factors to screen for potential DHTX readmission and/or protective factors to decrease the change for DHTX readmission.
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INTRODUCTION: The usage of extracorporeal membrane oxygenation (ECMO) in trauma patients has increased significantly within the past decade. Despite increased research on ECMO application in trauma patients, there remains limited data on factors predicting morbidity and mortality outcome. Therefore, the primary objective of this study is to describe patient characteristics that are independently associated with mortality in ECMO therapy in trauma patients, to further guide future research. METHODS: This retrospective study was conducted using the Trauma Quality Improvement Program database from 2010 to 2019. All adult (age ≥ 16 y) trauma patients that utilized ECMO were included. A Significant differences (P < 0.05) in demographic and clinical characteristics between groups were calculated using an independent t-test for normal distributed continuous values, a Mann-Whitney U test for non-normal distributed values, and a Pearson chi-square test for categorical values. A multivariable regression model was used to identify independent predictors for mortality. A survival flow chart was constructed by using the strongest predictive value for mortality and using the optimal cut-off point calculated by the Youden index. RESULTS: Five hundred forty-two patients were included of whom 205 died. Multivariable analysis demonstrated that the female gender, ECMO within 4 h after presentation, a decreased Glasgow Coma Scale, increased age, units of blood in the first 4 h, and abbreviated injury score for external injuries were independently associated with mortality in ECMO trauma patients. It was found that an external abbreviated injury score of ≥3 had the strongest predictive value for mortality, as patients with this criterion had an overall 29.5% increased risk of death. CONCLUSIONS: There is an ongoing increasing trend in the usage of ECMO in trauma patients. This study has identified multiple factors that are individually associated with mortality. However, more research must be done on the association between mortality and noninjury characteristics like Pao2/Fio2 ratio, acute respiratory distress syndrome classification, etc. that reflect the internal state of the patient.
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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 52â¯468 patients were included; the median (IQR) age was 29 (22-40) years, 46â¯189 patients (88.0%) were male, 31â¯470 (60.0%) were Black, and 20â¯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.
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BACKGROUND: Rib fractures represent a typical injury pattern in older people and are associated with respiratory morbidity and mortality. Regional analgesia modalities are adjuncts for pain management, but the optimal timing for their initiation remains understudied. We hypothesized that early regional analgesia would have similar outcomes to late regional analgesia. METHODS: We retrospectively reviewed the American College of Surgeons Trauma Quality Improvement Program database from 2017 to 2019. We included patients ≥65 years old admitted with blunt chest wall trauma who received regional analgesia. We divided patients into 2 groups: (1) early regional analgesia (within 24 hours of admission) and (2) late regional analgesia (>24 hours). The outcomes evaluated were ventilator-associated pneumonia, mortality, unplanned intensive care unit admission, unplanned intubation, discharge to home, and duration of stay. Univariable analysis and multivariable logistic regression adjusting for patient and injury characteristics, trauma center level, and respiratory interventions were performed. RESULTS: In the study, 2,248 patients were included. The mean (standard deviation) age was 75.3 (6.9), and 52.7% were male. The median injury severity score (interquartile range) was 13 (9-17). The early regional analgesia group had a decreased incidence of unplanned intubation (2.7% vs 5.3%, P = .002), unplanned intensive care unit admission (4.9% vs 8.4%, P < .001), and shorter mean duration of stay (5.5 vs 6.5 days, P = .002). In multivariable analysis, early regional analgesia was associated with decreased odds of unplanned intubation (odds ratio, 0.58; 95% confidence interval, 0.36-0.94; P = .026), unplanned intensive care unit admission (odds ratio, 0.60; 95% confidence interval, 0.041-0.86; P = .006), and increased odds of discharge to home (odds ratio, 1.27; 95% confidence interval, 1.04-1.55; P = .019). After multivariable adjustment, no significant difference was found for ventilator-associated pneumonia or mortality (odds ratio, 0.60; 95% confidence interval, 0.34-1.04; P = .070). CONCLUSION: Early regional analgesia initiation is associated with improved outcomes in older people with blunt chest wall injuries. Geriatric trauma care bundles targeting early initiation of regional analgesia can potentially decrease complications and resource use.
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Analgesia , Pneumonia Associada à Ventilação Mecânica , Fraturas das Costelas , Traumatismos Torácicos , Ferimentos não Penetrantes , Humanos , Masculino , Idoso , Feminino , Manejo da Dor , Estudos Retrospectivos , Traumatismos Torácicos/complicações , Fraturas das Costelas/complicações , Fraturas das Costelas/terapia , Ferimentos não Penetrantes/complicações , Escala de Gravidade do Ferimento , Tempo de InternaçãoRESUMO
We sought to study the role of circulating cellular clusters (CCC) -such as circulating leukocyte clusters (CLCs), platelet-leukocyte aggregates (PLA), and platelet-erythrocyte aggregates (PEA)- in the immunothrombotic state induced by COVID-19. Forty-six blood samples from 37 COVID-19 patients and 12 samples from healthy controls were analyzed with imaging flow cytometry. Patients with COVID-19 had significantly higher levels of PEAs (p value<0.001) and PLAs (p value = 0.015) compared to healthy controls. Among COVID-19 patients, CLCs were correlated with thrombotic complications (p value = 0.016), vasopressor need (p value = 0.033), acute kidney injury (p value = 0.027), and pneumonia (p value = 0.036), whereas PEAs were associated with positive bacterial cultures (p value = 0.033). In predictive in silico simulations, CLCs were more likely to result in microcirculatory obstruction at low flow velocities (≤1 mm/s) and at higher branching angles. Further studies on the cellular component of hyperinflammatory prothrombotic states may lead to the identification of novel biomarkers and drug targets for inflammation-related thrombosis.
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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.
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Inteligência Artificial , Cirurgiões , Humanos , Complicações Pós-Operatórias/epidemiologia , Complicações Pós-Operatórias/etiologia , Medição de Risco/métodos , PrognósticoRESUMO
OBJECTIVE: Current literature suggests that thoracic endovascular aortic repair (TEVAR) in older patients with aortic aneurysms results in higher peri-operative mortality and lower long term survival in females compared with males. However, sex related outcomes in younger patients with blunt thoracic aortic injury (BTAI) undergoing TEVAR remain unknown. This study examined the association between sex and outcomes after TEVAR for BTAI. METHODS: A retrospective cohort study was performed of all patients who underwent TEVAR for BTAI in the American College of Surgeons Trauma Quality Improvement Program (ACS-TQIP) between 2016 and 2019. The primary outcome was in hospital death. Secondary outcomes were peri-operative complications. Multivariable logistic regression was used to adjust for demographics, comorbidities, injury severity score, and aortic injury grade. RESULTS: Two thousand and twenty-two patients were included; 26% were female. Compared with males, females were older (46 [IQR 30, 62] vs. 39 [IQR 28, 56] years; p < .001), more often obese (41% vs. 33%; p = .005), had lower rates of alcohol use disorder (4.1% vs. 8.9%; p < .001) and a higher prevalence of hypertension (29% vs. 22%; p = .001). The injury severity was comparable between females and males (Injury Severity Score ≥ 25; 84% vs. 80%; p = .11) and there was no difference in aortic injury grades when comparing females with males (grade 1, 33% vs. 33%; grade 2, 24% vs. 25%; grade 3, 43% vs. 40%; grade 4, 0.8% vs. 1.3%; p = .53). Multivariable logistic regression demonstrated no difference for in hospital mortality between females and males (OR 1.02; 95% CI 0.67 - 1.53, p = .93). Compared with males, females were at lower risk of acute kidney injury (AKI) (OR 0.33; 95% CI 0.17 - 0.64; p = .001) and ventilator associated pneumonia (VAP) (OR 0.50; 95% CI 0.28 - 0.91; p = .023). CONCLUSION: This study did not demonstrate a sex related in hospital mortality difference following TEVAR for BTAI. However, female sex was associated with a lower risk of AKI and VAP. Future studies should evaluate sex differences and long term outcomes following TEVAR in patients with BTAI.
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Procedimentos Endovasculares , Traumatismos Torácicos , Lesões do Sistema Vascular , Ferimentos não Penetrantes , Humanos , Masculino , Feminino , Idoso , Correção Endovascular de Aneurisma , Aorta Torácica/diagnóstico por imagem , Aorta Torácica/cirurgia , Aorta Torácica/lesões , Mortalidade Hospitalar , Estudos Retrospectivos , Resultado do Tratamento , Procedimentos Endovasculares/métodos , Ferimentos não Penetrantes/etiologia , Traumatismos Torácicos/etiologia , Lesões do Sistema Vascular/etiologia , Fatores de RiscoRESUMO
BACKGROUND: Tiered trauma triage systems have resulted in a significant mortality reduction, but models have remained unchanged. The aim of this study was to develop and test an artificial intelligence algorithm to predict critical care resource utilization. METHODS: We queried the ACS-TQIP 2017-18 database for truncal gunshot wounds(GSW). An information-aware deep neural network (DNN-IAD) model was trained to predict ICU admission and need for mechanical ventilation (MV). Input variables included demographics, comorbidities, vital signs, and external injuries. The model's performance was assessed using the area under receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). RESULTS: For the ICU admission analysis, we included 39,916 patients. For the MV need analysis, 39,591 patients were included. Median (IQR) age was 27 (22,36). AUROC and AUPRC for predicting ICU need were 84.8 ± 0.5 and 75.4 ± 0.5, and the AUROC and AUPRC for MV need were 86.8 ± 0.5 and 72.5 ± 0.6. CONCLUSIONS: Our model predicts hospital utilization outcomes in patients with truncal GSW with high accuracy, allowing early resource mobilization and rapid triage decisions in hospitals with capacity issues and austere environments.
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Triagem , Ferimentos por Arma de Fogo , Humanos , Triagem/métodos , Inteligência Artificial , Ferimentos por Arma de Fogo/terapia , Cuidados Críticos , Hospitais , Estudos RetrospectivosRESUMO
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.
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COVID-19 , Embolia Pulmonar , Humanos , Tempo de Internação , COVID-19/epidemiologia , COVID-19/complicações , Mortalidade Hospitalar , Embolia Pulmonar/complicações , Resultado do Tratamento , Complicações Pós-Operatórias/etiologia , Estudos RetrospectivosRESUMO
As the coronavirus disease-19 (COVID-19) pandemic continues to evolve in 2022 with the surge of novel viral variants, it is important for physicians to understand and appreciate the surgical implications of the pandemic. This review provides an overview of the implications of the ongoing COVID-19 pandemic on surgical care and provides recommendations for perioperative management. Most observational studies suggest a higher risk for patients undergoing surgery with COVID-19 compared with risk-adjusted non-COVID-19 patients.
Assuntos
COVID-19 , Humanos , SARS-CoV-2 , PandemiasRESUMO
The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2, has quickly spread over the world since December 2019. COVID-19 is a systemic disease that can affect various organs throughout the body. Gastrointestinal (GI) symptoms have been reported in 16% to 33% of all patients with COVID-19 and in 75% of critically ill patients. This chapter reviews the GI manifestations of COVID-19 as well as their diagnostic and treatment modalities.