Assessing the utility of natural language processing for detecting postoperative complications from free medical text.

BACKGROUND: Postoperative complication rates are often assessed through administrative data, although this method has proven to be imprecise. Recently, new developments in natural language processing have shown promise in detecting specific phenotypes from free medical text. Using the clinical challenge of extracting four specific and frequently undercoded postoperative complications (pneumonia, urinary tract infection, sepsis, and septic shock), it was hypothesized that natural language processing would capture postoperative complications on a par with human-level curation from electronic health record free medical text. METHODS: Electronic health record data were extracted for surgical cases (across 11 surgical sub-specialties) from 18 hospitals in the Capital and Zealand regions of Denmark that were performed between May 2016 and November 2021. The data set was split into training/validation/test sets (30.0%/48.0%/22.0%). Model performance was compared with administrative data and manual extraction of the test data set. RESULTS: Data were obtained for 17 486 surgical cases. Natural language processing achieved a receiver operating characteristic area under the curve of 0.989 for urinary tract infection, 0.993 for pneumonia, 0.992 for sepsis, and 0.998 for septic shock, whereas administrative data achieved a receiver operating characteristic area under the curve of 0.595 for urinary tract infection, 0.624 for pneumonia, 0.571 for sepsis, and 0.625 for septic shock. CONCLUSION: The natural language processing approach was able to capture complications with acceptable performance, which was superior to administrative data. In addition, the model performance approached that of manual curation and thereby offers a potential pathway for complete real-time coverage of postoperative complications across surgical procedures based on natural language processing assessment of electronic health record free medical text.

Assessing optimal methods for transferring machine learning models to low-volume and imbalanced clinical datasets: experiences from predicting outcomes of Danish trauma patients.

Introduction: Accurately predicting patient outcomes is crucial for improving healthcare delivery, but large-scale risk prediction models are often developed and tested on specific datasets where clinical parameters and outcomes may not fully reflect local clinical settings. Where this is the case, whether to opt for de-novo training of prediction models on local datasets, direct porting of externally trained models, or a transfer learning approach is not well studied, and constitutes the focus of this study. Using the clinical challenge of predicting mortality and hospital length of stay on a Danish trauma dataset, we hypothesized that a transfer learning approach of models trained on large external datasets would provide optimal prediction results compared to de-novo training on sparse but local datasets or directly porting externally trained models. Methods: Using an external dataset of trauma patients from the US Trauma Quality Improvement Program (TQIP) and a local dataset aggregated from the Danish Trauma Database (DTD) enriched with Electronic Health Record data, we tested a range of model-level approaches focused on predicting trauma mortality and hospital length of stay on DTD data. Modeling approaches included de-novo training of models on DTD data, direct porting of models trained on TQIP data to the DTD, and a transfer learning approach by training a model on TQIP data with subsequent transfer and retraining on DTD data. Furthermore, data-level approaches, including mixed dataset training and methods countering imbalanced outcomes (e.g., low mortality rates), were also tested. Results: Using a neural network trained on a mixed dataset consisting of a subset of TQIP and DTD, with class weighting and transfer learning (retraining on DTD), we achieved excellent results in predicting mortality, with a ROC-AUC of 0.988 and an F2-score of 0.866. The best-performing models for predicting long-term hospitalization were trained only on local data, achieving an ROC-AUC of 0.890 and an F1-score of 0.897, although only marginally better than alternative approaches. Conclusion: Our results suggest that when assessing the optimal modeling approach, it is important to have domain knowledge of how incidence rates and workflows compare between hospital systems and datasets where models are trained. Including data from other health-care systems is particularly beneficial when outcomes are suffering from class imbalance and low incidence. Scenarios where outcomes are not directly comparable are best addressed through either de-novo local training or a transfer learning approach.

Genetic risk factors for postoperative atrial fibrillation-a nationwide genome-wide association study (GWAS).

Background: Atrial fibrillation (AF) is a major cause of morbidity with a high prevalence among the elderly and has an established genetic disposition. Surgery is a well-known risk factor for AF; however, it is currently not recognized how much common genetic variants influence the postoperative risk. The purpose of this study was to identify Single Nucleotide Polymorphisms associated with postoperative AF. Methods: The UK Biobank was utilized to conduct a Genome-Wide Association Study (GWAS) to identify variants associated with AF after surgery. An initial discovery GWAS was performed in patients that had undergone surgery with subsequent replication in a unique non-surgical cohort. In the surgical cohort, cases were defined as newly diagnosed AF within 30 days after surgery. The threshold for significance was set at 5 × 10-8. Results: After quality control, 144,196 surgical patients with 254,068 SNPs were left for analysis. Two variants (rs17042171 (p = 4.86 × 10-15) and rs17042081 (p = 7.12 × 10-15)) near the PITX2-gene reached statistical significance. These variants were replicated in the non-surgical cohort (1.39 × 10-101 and 1.27 × 10-93, respectively). Several other loci were significantly associated with AF in the non-surgical cohort. Conclusion: In this GWAS-analysis of a large national biobank, we identified 2 variants that were significantly associated with postoperative AF. These variants were subsequently replicated in a unique non-surgical cohort. These findings bring new insight in the genetics of postoperative AF and may help identify at-risk patients and guide management.

Assessing the utility of a sliding-windows deep neural network approach for risk prediction of trauma patients.

The risks of post trauma complications are regulated by the injury, comorbidities, and the clinical trajectories, yet prediction models are often limited to single time-point data. We hypothesize that deep learning prediction models can be used for risk prediction using additive data after trauma using a sliding windows approach. Using the American College of Surgeons Trauma Quality Improvement Program (ACS TQIP) database, we developed three deep neural network models, for sliding-windows risk prediction. Output variables included early- and late mortality and any of 17 complications. As patients moved through the treatment trajectories, performance metrics increased. Models predicted early- and late mortality with ROC AUCs ranging from 0.980 to 0.994 and 0.910 to 0.972, respectively. For the remaining 17 complications, the mean performance ranged from 0.829 to 0.912. In summary, the deep neural networks achieved excellent performance in the sliding windows risk stratification of trauma patients.

Assessing the utility of deep neural networks in detecting superficial surgical site infections from free text electronic health record data.

Background: High-quality outcomes data is crucial for continued surgical quality improvement. Outcomes are generally captured through structured administrative data or through manual curation of unstructured electronic health record (EHR) data. The aim of this study was to apply natural language processing (NLP) to chart notes in the EHR to accurately capture postoperative superficial surgical site infections (SSSIs). Methods: Deep Learning (DL) NLP models were trained on data from 389,865 surgical cases across all 11 hospitals in the Capital Region of Denmark. Surgical cases in the training dataset were performed between January 01st, 2017, and October 30th, 2021. We trained a forward reading and a backward reading universal language model on unlabeled postoperative chart notes recorded within 30 days of a surgical procedure. The two language models were subsequently finetuned on labeled data for the classification of SSSIs. Validation and testing were performed on surgical cases performed during the month of November 2021. We propose two different use cases: a stand-alone machine learning (SAM) pipeline and a human-in-the-loop (HITL) pipeline. Performances of both pipelines were compared to administrative data and to manual curation. Results: The models were trained on 3,983,864 unlabeled chart notes and finetuned on 1,231,656 labeled notes. Models had a test area under the receiver operating characteristic curves (ROC AUC) of 0.989 on individual chart notes and 0.980 on an aggregated case level. The SAM pipeline had a sensitivity of 0.604, a specificity of 0.996, a positive predictive value (PPV) of 0.763, and a negative predictive value (NPV) of 0.991. Prior to human review, the HITL pipeline had a sensitivity of 0.854, a specificity of 0.987, a PPV of 0.603, and a NPV of 0.997. Conclusion: The performance of the SAM pipeline was superior to administrative data, and significantly outperformed previously published results. The performance of the HITL pipeline approached that of manual curation.

Postoperative complications: an observational study of trends in the United States from 2012 to 2018.

BACKGROUND: Postoperative complications continue to constitute a major issue for both the healthcare system and the individual patient and are associated with inferior outcomes and higher healthcare costs. The objective of this study was to evaluate the trends of postoperative complication rates over a 7-year period. METHODS: The NSQIP datasets from 2012 to 2018 were used to assess 30-day complication incidence rates including mortality rate following surgical procedures within ten surgical subspecialties. Multivariable logistic regression was used to associate complication rates with dataset year, while adjusting for relevant confounders. RESULTS: A total of 5,880,829 patients undergoing major surgery were included. Particularly the incidence rates of four complications were found to be decreasing: superficial SSI (1.9 to 1.3%), deep SSI (0.6 to 0.4%), urinary tract infection (1.6 to 1.2%) and patient unplanned return to the operating room (3.1 to 2.7%). Incidence rate for organ/space SSI exhibited an increase (1.1 to 1.5%). When adjusted, regression analyses indicated decreased odds ratios (OR) through the study period years for particularly deep SSI OR 0.92 [0.92-0.93], superficial SSI OR 0.94 [0.94-0.94] and acute renal failure OR 0.96 [0.95-0.96] as the predictor variable (study year) increased (p < 0.01). However, OR's for organ/space SSI 1.05 [1.05-1.06], myocardial infarction 1.01 [1.01-1.02] and sepsis 1.01 [1.01-1.02] increased slightly over time (all p < 0.01). CONCLUSIONS: Incidence rates for the complications exhibited a stable trend over the study period, with minor in or decreases observed.

An assessment of the effect of the genotype on postoperative venous thromboembolism risk in 140,831 surgical patients.

BACKGROUND: Postoperative Venous Thromboembolism Events (VTE) constitute a major source of morbidity and mortality after surgery. The aim of this study was to investigate whether commonly occurring Single Nucleotide Polymorphisms (SNPs) are associated with VTE in the surgical setting. METHODS: Retrospective study using data from the United Kingdom (UK) biobank, a genome biobank containing healthcare and genotyping data from more than 500.000 individuals. A cohort of 140,831patients with a registered surgical procedure was identified and used for a discovery genome wide association study (GWAS), with the remainder of the cohort (305,349 non-surgical patients) used as a replication cohort. Primary outcome was associations between SNPs and VTE within 30 days after a surgical procedure. Genome wide significance was set at p = 5 × 10-8. RESULTS: In the surgical (discovery) cohort, no SNPs reached genome wide significance. The VTE association of the top candidate SNP in the ABO gene rs505922 (p = 3.33 × 10-7), was replicated in the general (replication) cohort (p = 2.42 × 10-59). CONCLUSIONS: and Relevance: This study did not identify associations between SNPs and postoperative VTE events reaching genome-wide significance, although the VTE relevance of top candidates were demonstrated.

Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study.

BACKGROUND: Early detection of postoperative complications, including organ failure, is pivotal in the initiation of targeted treatment strategies aimed at attenuating organ damage. In an era of increasing health-care costs and limited financial resources, identifying surgical patients at a high risk of postoperative complications and providing personalised precision medicine-based treatment strategies provides an obvious pathway for reducing patient morbidity and mortality. We aimed to leverage deep learning to create, through training on structured electronic health-care data, a multilabel deep neural network to predict surgical postoperative complications that would outperform available models in surgical risk prediction. METHODS: In this retrospective study, we used data on 58 input features, including demographics, laboratory values, and 30-day postoperative complications, from the American College of Surgeons (ACS) National Surgical Quality Improvement Program database, which collects data from 722 hospitals from around 15 countries. We queried the entire adult (≥18 years) database for patients who had surgery between Jan 1, 2012, and Dec 31, 2018. We then identified all patients who were treated at a large midwestern US academic medical centre, excluded them from the base dataset, and reserved this independent group for final model testing. We then randomly created a training set and a validation set from the remaining cases. We developed three deep neural network models with increasing numbers of input variables and so increasing levels of complexity. Output variables comprised mortality and 18 different postoperative complications. Overall morbidity was defined as any of 16 postoperative complications. Model performance was evaluated on the test set using the area under the receiver operating characteristic curve (AUC) and compared with previous metrics from the ACS-Surgical Risk Calculator (ACS-SRC). We evaluated resistance to changes in the underlying patient population on a subset of the test set, comprising only patients who had emergency surgery. Results were also compared with the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) calculator. FINDINGS: 5 881 881 surgical patients, with 2941 unique Current Procedural Terminology codes, were included in this study, with 4 694 488 in the training set, 1 173 622 in the validation set, and 13 771 in the test set. The mean AUCs for the validation set were 0·864 (SD 0·053) for model 1, 0·871 (0·055) for model 2, and 0·882 (0·053) for model 3. The mean AUCs for the test set were 0·859 (SD 0·063) for model 1, 0·863 (0·064) for model 2, and 0·874 (0·061) for model 3. The mean AUCs of each model outperformed previously published performance metrics from the ACS-SRC, with a direct correlation between increasing model complexity and performance. Additionally, when tested on a subgroup of patients who had emergency surgery, our models outperformed previously published POTTER metrics. INTERPRETATION: We have developed unified prediction models, based on deep neural networks, for predicting surgical postoperative complications. The models were generally superior to previously published surgical risk prediction tools and appeared robust to changes in the underlying patient population. Deep learning could offer superior approaches to surgical risk prediction in clinical practice. FUNDING: The Novo Nordisk Foundation.

Hemorrhage and saline resuscitation are associated with epigenetic and proteomic reprogramming in the rat lung.

BACKGROUND: Epigenetic changes have been described in trauma patients in the form of histone acetylation events, but whether DNA-methylation occurs remains unknown. We hypothesized that the combination of hemorrhage and saline resuscitation would alter DNA-methylation and associated proteomic profiles in the rat lung. METHODS: Ten rats were subjected to a pressure-controlled hemorrhage and resuscitation model consisting of hemorrhage to a mean arterial pressure (MAP) of 35mmHg for 90 minutes, followed by saline resuscitation to a MAP >70mmHg for 90 minutes (n=5) or sham (only anesthesia and cannulation). Lungs were harvested and subjected to reduced genome wide DNA-methylation analysis through bisulphite sequencing as well as proteomics analysis. Data was analyzed for differentially methylated regions and associated alterations in proteomic networks through a weighted correlation network analysis (WCNA). Pathway analysis was used to establish biological relevance of findings. RESULTS: Hemorrhage and saline resuscitation were associated with differential methylation of 353 sites across the genome compared to the sham group. Of these, 30 were localized to gene promoter regions, 31 to exon regions and 87 to intron regions. Network analysis identified an association between hemorrhage/resuscitation and DNA-methylation events located to genes involved in areas of endothelial and immune response signaling. The associated proteomic response was characterized by activations of mRNA processing as well as endothelial Nitric Oxide Synthase (eNOS) metabolism. CONCLUSION: We demonstrated an association between DNA-methylation and hemorrhage/saline resuscitation. These results suggest a potential role of DNA-methylation in the host response to injury.

Assessment of post-trauma complications in eight million trauma cases over a decade in the USA.

BACKGROUND: Trauma is associated with a significant risk of post-trauma complications (PTCs). These include thromboembolic events, strokes, infections, and failure of organ systems (eg, kidney failure). Although care of the trauma patient has evolved during the last decade, whether this has resulted in a reduction in specific PTCs is unknown. We hypothesize that the incidence of PTCs has been decreasing during a 10-year period from 2007 to 2017. METHODS: This is a descriptive study of trauma patients originating from level 1, 2, 3, and 4 trauma centers in the USA, obtained via the Trauma Quality Improvement Program (TQIP) database from 2007 to 2017. PTCs documented throughout the time frame were extracted along with demographic variables. Multiple regression modeling was used to associate admission year with PTCs, while controlling for age, gender, Glasgow Coma Scale score, and Injury Severity Score. RESULTS: Data from 8 720 026 trauma patients were extracted from the TQIP database. A total of 366 768 patients experienced one or more PTCs. There was a general decrease in the incidence of PTCs during the study period, with the overall incidence dropping from 7.0% in 2007 to 2.8% in 2017. Multiple regression identified a slight decrease in incidence in all PTCs, although deep surgical site infection (SSI), deep venous thrombosis (DVT), and stroke incidences increased when controlled for confounders. DISCUSSION: Overall the incidence of PTCs dropped during the 10-year study period, although deep SSI, DVT, stroke, and cardiac arrest increased during the study period. Better risk prediction tools, enabling a precision medicine approach, are warranted to identify at-risk patients. LEVEL OF EVIDENCE: III.

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics-Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

Identification of a new genetic variant associated with cholecystitis: A multicenter genome-wide association study.

BACKGROUND: The genomic landscape of gallbladder disease remains poorly understood. We sought to examine the association between genetic variants and the development of cholecystitis. METHODS: The Biobank of a large multi-institutional health care system was used. All patients with cholecystitis were identified using International Statistical Classification of Diseases, 10th Revision, codes and genotyped across six batches. To control for population stratification, data were restricted to that from individuals of European genomic ancestry using a multidimensional scaling approach. The association between single nucleotide polymorphisms and cholecystitis was evaluated with a mixed linear model-based analysis, controlling for age, sex, and obesity. The threshold for significance was set at 5 × 10. RESULTS: Of 24,635 patients (mean ± SD age, 60.1 ± 16.7 years; 13,022 females [52.9%]), 900 had cholecystitis (mean ± SD age, 65.4 ± 14.3 years; 496 females [55.1%]). After meta-analysis, three single nucleotide polymorphisms on chromosome 5p15 exceeded the threshold for significance (p < 5 × 10). The phenotypic variance of cholecystitis explained by genetics and controlling for sex and obesity was estimated to be 17.9%. CONCLUSION: Using a multi-institutional genomic Biobank, we report that a region on chromosome 5p15 is associated with the development of cholecystitis that can be used to identify patients at risk. LEVEL OF EVIDENCE: Prognostic and epidemiological, Level III.

Gallbladder wall thickness as a predictor of intraoperative events during laparoscopic cholecystectomy: A prospective study of 1089 patients.

BACKGROUND: Laparoscopic cholecystectomy (LC) has a wide range of technical difficulty. Preoperative risk stratification is essential for adequate planning and patient counseling. We hypothesized that gallbladder wall thickness (GWT) is more objective marker than symptom duration in predicting complexity, as determined by operative time (OT), intraoperative events (IE), and postoperative complications. METHODS: All adult patients who underwent LC during 2010-2018 were included. GWT, measured on imaging and on the histopathologic exam, was divided into three groups: <3 mm (normal), 3-7 mm and >7 mm. Univariate and multivariable analyses were performed to determine the association between GWT and 1) operative time, 2) the incidence of IE and 3) postoperative outcomes. RESULTS: A total of 1089 patients, subjects to LC, were included in the study. GWT was positively correlated with median OT (p < 0.001), the incidence of IE (p < 0.001) and median length of hospital stay (p < 0.001). GWT independently predicted IE (OR = 2.1 95% CI: 1.3-3.4) and outperformed symptom duration, which was not significantly associated with any of the outcomes (p = 0.7). CONCLUSIONS: GWT independently predicted IE and may serve as an objective marker of LC complexity.

Bilateral internal iliac artery embolization for pelvic trauma: Effectiveness and safety.

INTRODUCTION: Bilateral internal iliac artery embolization (BIIAE) with temporary embolic materials epitomizes damage-control principles in the treatment of exsanguinating hemorrhage from pelvic trauma. However, instances of ischemic complications have been reported. The aim of our study was to assess safety and effectiveness of BIIAE. METHODS: All patients who received BIIAE for pelvic trauma at a Level I Trauma Center between 1998 and 2018 were reviewed. Effectiveness was assessed by radiographic bleeding control and clinical bleeding control, i.e. stabilization of vital signs and reduction in blood transfusion. Safety was assessed by any evidence for ischemic damage of pelvic organs or tissues. RESULTS: Of 61 patients undergoing BIIAE, bleeding control was confirmed radiographically in 60 (98%) and clinically in 55 (90%), including 4 (7%) patients who required repeat embolization. Six (10%) patients died due to insufficient pelvic bleeding control. No BIIAE-related complications were identified. CONCLUSION: The overall clinical effectiveness and safety rates of BIIAE for pelvic bleeding control, when combined with other methods of hemostasis, were 90% and 100% respectively.

Contemporary management of penetrating renal trauma - A national analysis.

INTRODUCTION: Indications for nonoperative management (NOM) after penetrating renal injury remain ill-defined. Using a national database, we sought to describe the experience of operative and nonoperative management in the United States and retrospectively examine risk factors for failure of NOM. MATERIALS AND METHODS: The TQIP database 2010-2016 was used to identify patients with penetrating renal trauma. Outcomes of patients treated with an immediate operation (IO) and NOM are described. Failure of NOM was defined as the need for a renal operation after 4 h from arrival. Univariate then multivariable regression analyses were performed to identify predictors of NOM failure. RESULTS: Out of 8139 patients with kidney trauma, 1,842 had a penetrating mechanism of injury and were included. Of those, 89% were male, median age was 28 years, and 330 (18%) were offered NOM. Compared to IO, NOM patients were less likely to have gunshot wound (59% vs 89% p < 0.001) or high-grade renal injuries [AAST 4-5] (48% vs 76%, p < 0.001). Lower rates of in-hospital complications and shorter ICU and hospital stays were observed in the NOM group. NOM failed in 26 patients (8%). Independent predictors of NOM failure included a concomitant abdominal injury (OR = 3.99, 95% CI 1.03-23.23, p = 0.044), and every point increase in AAST grade (OR = 2.43, 95% CI 1.27-5.21, p = 0.005). CONCLUSIONS: NOM is highly successful in selected patients. Concomitant abdominal injuries and higher grade AAST injuries predict NOM failure and should be considered when selecting patients for IO or NOM.

Refugee Access to Surgical Care in Lebanon: A Post Hoc Analysis of the SCAR Study.

BACKGROUND: Lebanon hosts an estimated one million Syrian refugees registered with the United Nations High Commissioner for Refugees (UNHCR). The UNHCR contracts with select Lebanese hospitals to provide affordable primary and emergency care to refugees. We aimed to assess the surgical capabilities of UNHCR-affiliated hospitals in Lebanon. METHODS: Cross-sectional data from the Surgical Capacity in Areas with Refugees study were combined with hospital affiliation data obtained from the UNHCR. The Surgical Capacity in Areas with Refugees study evaluated surgical capacity in Lebanon by mapping all acute care hospitals and administering the five domain Personnel, Infrastructure, Procedures, Equipment, and Supplies (PIPES) tool to each hospital. Mean PIPES indices and mean numbers of hospital beds, surgeons, and anesthesiologists were compared between UNHCR-affiliated and nonaffiliated hospitals. Geographically, the distribution of UNHCR-affiliated hospitals was cross-referenced with refugee population distributions. RESULTS: One hundred and twenty nine hospitals were included, 35 (27.1%) of which were affiliated with the UNHCR. The PIPES tool was administered across all hospitals. Mean PIPES indices and mean number of hospital beds, general surgeons, and anesthesiologists were similar between UNHCR-affiliated and nonaffiliated hospitals. Geographical mapping of hospitals and refugee populations across Lebanon revealed a disparity in the Northeastern region of the country: that region had the highest number of refugees but lacked sufficient UNHCR coverage. CONCLUSIONS: Hospitals covered by the UNHCR performed similarly to nonaffiliated hospitals with respect to all aspects of the PIPES surgical capacity tool. However, there is a concerning geographic mismatch between UNHCR coverage and refugee density, specifically in the governorates of Akkar, Bekaa, and Baalbek-Hermel.

Hartmann's Procedure vs Primary Anastomosis with Diverting Loop Ileostomy for Acute Diverticulitis: Nationwide Analysis of 2,729 Emergency Surgery Patients.

BACKGROUND: Recent small randomized trials suggest that primary anastomosis with a diverting loop ileostomy (PADLI) is a safe alternative to Hartmann's procedure (HP) for patients with acute diverticulitis necessitating emergent operation. We sought to examine the 30-day outcomes of patients undergoing emergent HP vs PADLI. METHOD: Using the American College of Surgeons NSQIP Colectomy Procedure Targeted Database from 2012 to 2016, all patients with acute diverticulitis who underwent emergent HP or PADLI were identified. Multivariable logistic models were constructed to compare the 30-day mortality, overall morbidity, and individual postoperative complications (eg surgical site infection, bleeding, sepsis) of the 2 procedures, controlling for all preoperative variables (eg demographics, comorbidities, laboratory values, illness severity), as well as intraoperative and procedure-specific variables (eg wound classification). RESULTS: Of 130,963 patients, 2,729 patients were included. Median age was 64 years, 48.5% were male; the majority of patients underwent HP and only 208 (7.6%) underwent PADLI. Hartmann's procedure patients had more comorbidities (eg COPD: 9.8% vs 4.8%; p = 0.017), were more functionally dependent (6.3% vs 2.4%; p = 0.025), and were sicker (eg septic shock: 11.1% vs 5.3%; p = 0.015) compared with PADLI patients. The mortality rates for HP vs PADLI were 7.6% and 2.9%, respectively (p = 0.011). The morbidity rates were 55.4% and 48.6%, respectively (p = 0.056). In multivariable analyses, compared with HP, PADLI did not result in increased rates of mortality (odds ratio 0.21; 95% CI 0.03 to 1.58; p = 0.129) or morbidity (odds ratio 0.96; 95% CI 0.63 to 1.45; p = 0.834). The odds of most major postoperative complications were also similar for HP and PADLI overall. CONCLUSIONS: Currently, surgeons perform HP more frequently than PADLI. When controlling for patient population differences, PADLI appears to be at least a safe alternative to HP for select patient populations needing emergent surgical management of acute diverticulitis.

The effect of fluid resuscitation strategy on monocyte and T-cell surface markers.

BACKGROUND: Despite initial lifesaving benefits, posttraumatic resuscitation strategies have been associated with immunologic complications leading to systemic inflammatory response syndrome, sepsis, multiple organ failure, and late trauma death. Nevertheless, the direct effect on immunologic surface markers remains inadequately described. We hypothesized that changes in monocyte and T-cell surface markers were associated with initial posttraumatic fluid resuscitation. MATERIALS AND METHODS: Data were extracted from the inflammation and host response to injury (Glue Grant) study. Blood samples were drawn from 492 patients on days 0, 1, 4, 7, 14, and 28 and analyzed for 31 monocyte and T-cell surface markers. Resuscitation strategies during the initial 48 h were quantified, including transfusion of packed red blood cells (PRBCs), fresh frozen plasma (FFP), platelets, and crystalloids. Longitudinal surface marker concentration changes were quantified by the calculation of a within-patient signal intensity change and were associated with resuscitation strategy while controlling confounders. P-values were post hoc corrected using the false detection rate q-value. RESULTS: The monocyte surface marker (CD83) trajectory (as measured by a within-patient signal intensity change) was found to be positively associated with volume of PRBCs transfused (q = 0.002) and negatively associated with the transfused volume of FFP (q = 0.004). T-cell surface marker (CD3) was found to be negatively associated with volume of PRBCs transfused (q = 854 × 10-9) and positively associated with the transfused volume of FFP (q = 0.022). Platelets and crystalloid transfusion volumes were not associated with any surface marker trajectories. CONCLUSIONS: PRBC and FFP transfusion was associated with opposing effects on CD3 and CD83 trajectories, which may in part explain some of the protective effects of a high FFP:PRBC ratio in trauma-related resuscitation.

[Lengthening temporalis myoplasty in treatment of chronic facial paralysis].

Introducing the lengthening temporalis myoplasty (LTM), a newly implemented surgical treatment of chronic facial paralysis. LTM is a single-stage operation where the temporalis muscle is transposed for dynamic smile reconstruction, hereby serving as an alternative to the more complex two-stage microvascular functional muscle transplantation. This case report demonstrates how LTM can be used to treat patients, who are not motivated or suitable for extensive surgery. The introduction of this technique aims to help a larger number of patients with chronic facial paralysis.

The effect of resuscitation strategy on the longitudinal immuno-inflammatory response to blunt trauma.

INTRODUCTION: Resuscitation strategies following blunt trauma have been linked to immuno-inflammatory complications leading to systemic inflammatory syndrome (SIRS), sepsis and multiple organ failure (MOF). The effect of resuscitation strategy on longitudinal inflammation marker trajectories is, however, unknown. We hypothesized that the effect of resuscitation strategy extends beyond the trauma-related coagulopathy, perhaps affecting the longitudinal immuno-inflammatory response to injury. METHODS: We analyzed data prospectively collected for the Inflammation and Host Response to Injury (Glue Grant) study. Blood sampling for inflammation marker analyses from blunt trauma patients was done on admission days 0, 1, 4, 7, 14, 21 and 28 where applicable. Total volume transfused of packed red blood cells (PRBC), fresh frozen plasma (FFP), platelets (PLT), and crystalloids during the initial 48h was extracted, along with an analysis for an array of cytokines by Enzyme Linked Immunosorbent Assay (ELISA) technique. A within patient concentration change (WPCC) was calculated to quantify longitudinal alterations in cytokine levels, while controlling for potential confounders. To account for the multiple comparisons performed, p-values obtained from the multivariate regression model were post-hoc corrected by the false detection rate (FDR) q-value. RESULTS: No longitudinal trajectories of inflammatory markers were found to be associated with PRBC- or PLT transfusion. Three proinflammatory cytokines (Il-1ß, MIP-1ß, and TNFR2) were negatively associated with volume of FFP transfused (q=0.02, q<0.001 and q=0.007 respectively), and one proinflammatory cytokine (MIP-1ß) was positively associated with crystalloid infusion (q=0.005). CONCLUSIONS: Resuscitation strategy employed following blunt trauma has limited association to longitudinal inflammation marker trajectories, with a potential association between the strategy employed and IL-1ß, TNFR2, and MIP-1ß trajectories, respectively.