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Postoperative infections constitute more than half of all postoperative complications. Surveillance of these complications is primarily done through manual chart review, which is time consuming, expensive, and typically only covers 10% to 15% of all operations. Automated surveillance would permit the timely evaluation of and reporting of all operations.The goal of this study was to develop and validate parsimonious, interpretable models for conducting surveillance of postoperative infections using structured electronic health records data. This was a retrospective study using 30,639 unique operations from 5 major hospitals between 2013 and 2019. Structured electronic health records data were linked to postoperative outcomes data from the American College of Surgeons National Surgical Quality Improvement Program. Predictors from the electronic health records included diagnoses, procedures, and medications. Infectious complications included surgical site infection, urinary tract infection, sepsis, and pneumonia within 30 days of surgery. The knockoff filter, a penalized regression technique that controls type I error, was applied for variable selection. Models were validated in a chronological held-out dataset.Seven percent of patients experienced at least one type of postoperative infection. Models selected contained between 4 and 8 variables and achieved0.91 area under the receiver operating characteristic curve,81% specificity,87% sensitivity,99% negative predictive value, and 10% to 15% positive predictive value in a held-out test dataset.Surveillance and reporting of postoperative infection rates can be implemented for all operations with high accuracy using electronic health records data and simple linear regression models.

Postoperative bleeding complications surveillance is done primarily through manual chart review. The purpose of this study was to develop and validate a detection model for postoperative bleeding complications using structured electronic health records data.Patients who underwent operations at 1 of 5 hospitals within our local health system between 2013 and 2019 and whose complications were reported by the American College of Surgeons National Surgical Quality Improvement Program were included. Electronic health records data were linked to American College of Surgeons National Surgical Quality Improvement Program data using personal health identifiers. Electronic health records predictors included diagnosis codes mapped to PheCodes, procedure names, and medications within 30 days after surgery. We defined bleeding events as the transfusion of red blood cell components within 30 days after surgery. The knockoff filter and the lasso were used to develop a model in a training set of operations from January 2013 to March 2017. Performance of each model was tested in a held-out data set of patients who underwent operations from March 2017 to October 2019.A total of 30,639 patients were included; 1,112 patients (3.6%) had a bleeding event. Eight predictor variables were selected by the knockoff filter. When applied to the test set, specificity was 94%, sensitivity was 94%, area under the curve was 0.97, and accuracy was 93%. Calibration was consistent in lower predicted risk patients, whereas the model slightly overpredicted risk in high-risk patients.We created a parsimonious, accurate model for identifying patients with bleeding complications. This model can be used to augment manual chart review for surveillance and reporting of perioperative bleeding complications, enabling inclusion of all surgeries in quality improvement efforts.

Unplanned hospital admission after intended outpatient surgery is an undesirable outcome. We aimed to develop a prediction model that estimates a patient's risk of conversion from outpatient surgery to inpatient hospitalization.This was a retrospective analysis using the American College of Surgeons National Surgical Quality Improvement Program database, 2005 to 2018. Conversion from outpatient to inpatient surgery was defined as having outpatient surgery and1 day hospital stay. The Surgical Risk Preoperative Assessment System was developed using multiple logistic regression on a training dataset (2005-2016) and compared to a model using the 26 relevant variables in the American College of Surgeons National Surgical Quality Improvement Program. The Surgical Risk Preoperative Assessment System was validated using a testing dataset (2017-2018). Performance statistics and Hosmer-Lemeshow plots were compared. Two high-risk definitions were compared: (1) the maximum Youden index, and (2) the cohort above the tenth decile of risk on the Hosmer-Lemeshow plot. The sensitivities, specificities, positive predictive values, negative predictive values, and accuracies were compared.In all, 2,822,379 patients were included; 3.6% of patients unexpectedly converted to inpatient. The 6-variable Surgical Risk Preoperative Assessment System model performed comparably to the 26-variable American College of Surgeons National Surgical Quality Improvement Program model (c-indices = 0.818 vs. 0.823; Brier scores = 0.0308 vs 0.0306, respectively). The Surgical Risk Preoperative Assessment System performed well on internal validation (c-index = 0.818, Brier score = 0.0341). The tenth decile of risk definition had higher specificity, positive predictive values, and accuracy than the maximum Youden index definition, while having lower sensitivity.The Surgical Risk Preoperative Assessment System accurately predicted a patient's risk of unplanned outpatient-to-inpatient conversion. Patients at higher risk should be considered for inpatient surgery, while lower risk patients could safely undergo operations at ambulatory surgery centers.

Background The COVID-19 pandemic has uniquely affected the United States. We hypothesize that transplantation would be uniquely affected. Methods In this population-based cohort study, adult transplantation data were examined as time series data. Autoregressive-integrated-moving-average models of transplantation rates were developed using data from 1990 to 2019 to forecast the 2020 expected rates in a theoretical scenario if the pandemic did not occur to generate observed-to-expected (O/E) ratios. Results 32,594 transplants were expected in 2020, and only 30,566 occurred (O/E 0.94, CI 0.88–0.99). 58,152 waitlist registrations were expected and 50,241 occurred (O/E 0.86, CI 0.80–0.94). O/E ratios of transplants were kidney 0.92 (0.86–0.98), liver 0.96 (0.89–1.04), heart 1.05 (0.91–1.23), and lung 0.92 (0.82–1.04). O/E ratios of registrations were kidney 0.84 (0.77–0.93), liver 0.95 (0.86–1.06), heart 0.99 (0.85–1.18), and lung 0.80 (0.70–0.94). Conclusions The COVID-19 pandemic was associated with a significant deficit in transplantation. The impact was strongest in kidney transplantation and waitlist registration.

Surgical Risk Preoperative Assessment System (SURPAS) estimates patient's preoperative risk of 12 postoperative complications, yet little is known about associations between these probabilities- We sought to examine relationships between predicted probabilities.Risk of 12 postoperative complications was calculated using SURPAS and the 2012-2018 ACS-NSQIP database. Pearson correlation coefficients (r) were computed to examine relationships between predicted outcomes. "High-risk" was predicted risk in the 10th decile.4,777,267 patients were included. 71.1% were not high risk, 10.7% were high risk for 1, and 18.2% were high risk for ≥2 complications. High mortality risk was associated with high risk for pulmonary (r = 0.94), cardiac (r = 0.98), renal (r = 0.93), and stroke (0.96) complications. Patients high-risk for ≥2 complications had the most comorbidities and actual adverse outcomes.High preoperative risk for certain postoperative complications had strong correlations. 18.2% of patients were high-risk for ≥2 complications and could be targeted for risk reduction interventions.

Emerging literature suggests that measures of social vulnerability should be incorporated into surgical risk calculators. The Social Vulnerability Index (SVI) is a measure designed by the CDC that encompasses 15 socioeconomic and demographic variables at the census tract level. We examined whether adding the SVI into a parsimonious surgical risk calculator would improve model performance.The eight-variable Surgical Risk Preoperative Assessment System (SURPAS), developed using the entire American College of Surgeons (ACS) NSQIP database, was applied to local ACS-NSQIP data from 2012 to 2018 to predict 12 postoperative outcomes. Patient addresses were geocoded and used to estimate the SVI, which was then added to the model as a ninth predictor variable. Brier scores and c-indices were compared for the models with and without the SVI.The analysis included 31,222 patients from five hospitals. Brier scores were identical for eight outcomes and improved by only one to two points in the fourth decimal place for four outcomes with addition of the SVI. Similarly, c-indices were not significantly different (p values ranged from 0.15 to 0.96). Of note, the SVI was associated with most of the eight SURPAS predictor variables, suggesting that SURPAS may already indirectly capture this important risk factor.The eight-variable SURPAS prediction model was not significantly improved by adding the SVI, showing that this parsimonious tool functions well without including a measure of social vulnerability.

Considerable variability exists between surgeons' assessments of a patient's individual pre-operative surgical risk. Surgical risk calculators are not routinely used despite their validation. We sought to compare thoracic surgeons' prediction of patients' risk of postoperative adverse outcomes versus a surgical risk calculator, the Surgical Risk Preoperative Assessment System (SURPAS). We developed vignettes from 30 randomly selected patients who underwent thoracic surgery in the American College of Surgeons' National Surgical Quality Improvement Program (NSQIP) database. Twelve thoracic surgeons estimated patients' preoperative risks of postoperative morbidity and mortality. These were compared to SURPAS estimates of the same vignettes. C-indices and Brier scores were calculated for the surgeons' and SURPAS estimates. Agreement between surgeon estimates was examined using intraclass correlation coefficients (ICCs). Surgeons estimated higher morbidity risk compared to SURPAS for low-risk patients (ASA classes 1-2, 11.5% vs. 5.1%, p=

Background The universal Surgical Risk Preoperative Assessment System (SURPAS) prediction models for postoperative adverse outcomes have good accuracy for estimating risk in broad surgical populations and for surgical specialties. The accuracy in individual operations has not yet been assessed. The objective of this study was to evaluate the Surgical Risk Preoperative Assessment System in predicting adverse outcomes for selected individual operations. Methods The SURPAS models were applied to the top 2 most frequent common procedural terminology codes in 9 surgical specialties and 5 additional common general surgical operations in the 2009 to 2018 database of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). Goodness of fit statistics were estimated, including c-indices for discrimination, Hosmer-Lemeshow graphs and P values for calibration, overall observed versus expected event rates, and Brier scores. Results The total sample size was 2,020,172, which represented 29% of the 6.9 million operations in the ACS NSQIP database. Average c-indices across 12 outcomes were acceptable (≥0.70) for 13 (56.5%) of the 23 operations. Overall observed-to-expected rates were similar for mortality and overall morbidity across the 23 operations. Hosmer-Lemeshow graphs over quintiles of risk comparing observed-to-expected rates of mortality and overall morbidity were similar for 52% and 70% of operations, respectively. Model performance was better in less complex operations and those done in patients with lower preoperative risk. Conclusion SURPAS displayed accuracy in estimating postoperative adverse events for some of the 23 operations studied, but not all. In the procedures where SURPAS was not accurate, developing disease or operation-specific risk models might be appropriate.

Background Residents report that faculty preference is a significant driver of opioid prescribing practices. This study compared opioid prescribing preferences of surgical residents and faculty against published guidelines and actual practice and assessed perceptions in communication and transparency around these practices. Methods Surgical residents and faculty were surveyed to evaluate the number of oxycodone tablets prescribed for common procedures. Quantities were compared between residents, faculty, Opioid Prescribing Engagement Network guidelines, and actual opioids prescribed. Frequency with which faculty communicate prescribing preferences and the desire for feedback and transparency in prescription practices were assessed. Results Fifty-six (72%) residents and 57 (59%) faculty completed the survey. Overall, faculty preferred a median number of tablets greater than recommended by Opioid Prescribing Engagement Network in 5 procedures, while residents did so in 9 of 14 procedures. On average, across all operations, faculty reported prescribing practices compliant with Opioid Prescribing Engagement Network 56.1% of the time, whereas residents did so 47.6% of the time (P = .40). Interestingly, opioids actually prescribed were significantly less than recommended in 7 procedures. Among faculty, 62% reported often or always specifying prescription preferences to residents, while only 9% of residents noted that faculty often did so. Residents (80%) and faculty (75%) were amenable to seeing regular reports of personal opioid prescription practices, and 74% and 65% were amenable to seeing practices compared with peers. Only 34% of residents and 44% of faculty wanted prescription practices made public. Conclusion There is a disconnect between opioid prescribing preferences and practice among surgical residents and faculty. Increased transparency through individualized reports and education regarding Opioid Prescribing Engagement Network guidelines with incorporation into the electronic medical record as practice advisories may reduce prescription variability.

Present at the time of surgery (PATOS) is an important measure to collect in postoperative complication surveillance systems because it may affect a patient's risk of a subsequent complication and the estimation of postoperative complication rates attributed to the healthcare system. The American College of Surgeons (ACS) NSQIP started collecting PATOS data for 8 postoperative complications in 2011, but no one has used these data to quantify how this may affect unadjusted and risk-adjusted postoperative complication rates.This study was a retrospective observational study of the ACS NSQIP database from 2012 to 2018. PATOS data were analyzed for the 8 postoperative complications of superficial, deep, and organ space surgical site infection; pneumonia; urinary tract infection; ventilator dependence; sepsis; and septic shock. Unadjusted postoperative complication rates were compared ignoring PATOS vs taking PATOS into account. Observed to expected ratios over time were also compared by calculating expected values using multiple logistic regression analyses with complication as the dependent variable and the 28 nonlaboratory preoperative variables in the ACS NSQIP database as the independent variables.In 5,777,108 patients, observed event rates for each outcome were reduced by between 6.1% (superficial surgical site infection) and 52.5% (sepsis) when PATOS was taken into account. The observed to expected ratios were similar each year for all outcomes, except for sepsis and septic shock in the early years.Taking PATOS into account is important for reporting unadjusted event rates. The effect varied by type of complication-lowest for superficial surgical site infection and highest for sepsis and septic shock. Taking PATOS into account was less important for risk-adjusted outcomes (observed to expected ratios), except for sepsis and septic shock.

Background Antibiotics are one of the leading causes of emergency room visits for adverse drug events, yet surveillance for antimicrobial allergy adverse events is limited and identifying true cases is challenging. As part of a larger study to improve antimicrobial use, we sought to develop and validate a tool for near real-time measurement of antimicrobial allergy adverse events. Methods An existing cohort of patients undergoing cardiac device procedures with known antimicrobial exposure was split into a development and validation set. Candidate triggers for identifying allergic reactionswere identified a priori, using disease phenotype codes “phecodes”, allergy documentation on allergy module of the electronic medical record (EMR), and keyword searches applied to clinical notes (e.g., “anaphylaxis,” “rash”), medication administration (e.g, corticosteroids alone or with antihistamines) and administrative codes (ICD-10 codes and phecodes). Cases were reviewed for presence of a true event, and the tool was iteratively updated based on chart review findings. The tool was then applied to the validation cohort and a sample of trigger-flagged and unflagged cases underwent manual review. Data were analyzed in SAS and model triggers were selected using a LASSO technique. Results Among 34,703 patients, N=431 cases underwent manual review (350 development; 120 validation), and 104 true allergy adverse events were identified. Among chart reviewed cases, the most frequently detected flags were keywords in unstructured clinical notes (35%), phecodes (26%), corticosteroid administration (15%), observed allergy documentation in EMR (14%) and reported allergy documentation in EMR (13%). The final model contained 7 triggers and had an AUC of 0.95, and a positive predictive value of 67% (Figure). The strongest predictors of true adverse events were the allergy health factors (aOR 358, 95% CI 76.3-999) and specific Phecodes (Table1). Conclusion We developed an antibiotic allergy measurement tool using structured and unstructured data that can be applied to detect antimicrobial adverse events in near-real time. This model may be applied to provide near real-time feedback to clinicians about antimicrobial allergy adverse events and may be useful for antimicrobial stewardship programs. Disclosures Westyn Branch-Elliman, MD, MMSc, DLA Piper,LLC/Medtronic: Advisor/Consultant|Gilead Pharmaceuticals: Grant/Research Support.

Completion lymph node dissection (CLND) was the standard treatment for patients with melanoma with positive sentinel lymph nodes (SLN) until 2017 when data from the DeCOG-SLT and MLST-2 randomized trials challenged the survival benefit of this procedure. We assessed the contribution of patient, tumor and facility factors on the use of CLND in patients with surgically resected Stage III melanoma.Using the National Cancer Database, patients who underwent surgical excision and were found to have a positive SLN from 2012 to 2017 were included. A multivariable mixed-effects logistic regression model with a random intercept for the facility was used to determine the effect of patient, tumor, and facility variables on the risk of CLND. Reference effect measures (REMs) were used to compare the contribution of contextual effects (unknown facility variables) versus measured variables on the variation in CLND use.From 2012 to 2017, the overall use of CLND decreased from 59.9% to 26.5% (p 0.0001). Overall, older patients and patients with government-based insurance were less likely to undergo CLND. Tumor factors associated with a decreased rate of CLND included primary tumor location on the lower limb, decreasing depth, and mitotic rate1. However, the contribution of contextual effects to the variation in CLND use exceeded that of the measured facility, tumor, time, and patient variables.There was a decrease in CLND use during the study period. However, there is still high variability in CLND use, mainly driven by unmeasured contextual effects.

Background The Surgical Risk Preoperative Assessment System (SURPAS) uses eight variables to accurately predict postoperative complications but has not been sufficiently studied in emergency surgery. We evaluated SURPAS in emergency surgery, comparing it to the Emergency Surgery Score (ESS). Methods SURPAS and ESS estimates of 30-day mortality and overall morbidity were calculated for emergency operations in the 2009–2018 ACS-NSQIP database and compared using observed-to-expected plots and rates, c-indices, and Brier scores. Cases with incomplete data were excluded. Results In 205,318 emergency patients, SURPAS underestimated (8.1%; 35.9%) while ESS overestimated (10.1%; 43.8%) observed mortality and morbidity (8.9%; 38.8%). Each showed good calibration on observed-to-expected plots. SURPAS had better c-indices (0.855 vs 0.848 mortality; 0.802 vs 0.755 morbidity), while the Brier score was better for ESS for mortality (0.0666 vs. 0.0684) and for SURPAS for morbidity (0.1772 vs. 0.1950). Conclusions SURPAS accurately predicted mortality and morbidity in emergency surgery using eight predictor variables.

The purpose of this study was to develop and test personalized predictions for functional recovery after Total Knee Arthroplasty (TKA) surgery, using a novel neighbors-based prediction approach. We used data from 397 patients with TKA to develop the prediction methodology and then tested the predictions in a temporally distinct sample of 202 patients. The Timed Up and Go (TUG) Test was used to assess physical function. Neighbors-based predictions were generated by estimating an index patient’s prognosis from the observed recovery data of previous similar patients (a.k.a., the index patient’s “matches”). Matches were determined by an adaptation of predictive mean matching. Matching characteristics included preoperative TUG time, age, sex and Body Mass Index. The optimal number of matches was determined to be m = 35, based on low bias (− 0.005 standard deviations), accurate coverage (50% of the realized observations within the 50% prediction interval), and acceptable precision (the average width of the 50% prediction interval was 2.33 s). Predictions were well-calibrated in out-of-sample testing. These predictions have the potential to inform care decisions both prior to and following TKA surgery.

This study was designed to: (1) characterize longitudinal patient-reported outcomes (PROs) between breast cancer patients undergoing lumpectomy and mastectomy and (2) compare return to baseline scores at 3 months and 6 months postoperatively. Newly diagnosed breast cancer patients seen at an academic breast center between June 2019 and February 2021 were invited to participate in longitudinal PRO surveys at their initial clinic visit. If willing to participate, patients were emailed the validated BREAST-Q™ questionnaire at the initial clinic visit (baseline), 2 weeks after surgery, and then every 3 months for the first year. We used linear mixed models to estimate the differences in slopes over time between lumpectomy and mastectomy for each PRO measure. Pearson’s Chi-square tests with Yates’ continuity correction were used to compare proportions of patients who return to baseline PRO scores. P < 0.05 was considered significant. Of 164 patients invited to participate, 100 (61%) completed a baseline survey and were included in analyses. Mastectomy patients had significantly greater decreases in breast satisfaction (P = 0.002), psychosocial well-being (P < 0.0001), and sexual well-being (P < 0.0001) over time compared with lumpectomy patients. Both surgical groups reported a decrease in physical well-being, although the decline was more significant in lumpectomy patients (P = 0.005). At 3 months and 6 months after surgery, significantly larger proportions of lumpectomy patients returned to their baseline breast satisfaction, psychosocial well-being, and physical well-being compared with mastectomy patients. Understanding how outcomes important to patients change over the care continuum can provide opportunities for early intervention and may prevent debilitating long-term morbidities of treatment.

Background: Cluster-randomized trials allow for the evaluation of a community-level or group-/cluster-level intervention. For studies that require a cluster-randomized trial design to evaluate cluster-level interventions aimed at controlling vector-borne diseases, it may be difficult to assess a large number of clusters while performing the additional work needed to monitor participants, vectors, and environmental factors associated with the disease. One such example of a cluster-randomized trial with few clusters was the “efficacy and risk of harms of repeated ivermectin mass drug administrations for control of malaria” trial. Although previous work has provided recommendations for analyzing trials like repeated ivermectin mass drug administrations for control of malaria, additional evaluation of the multiple approaches for analysis is needed for study designs with count outcomes. Methods: Using a simulation study, we applied three analysis frameworks to three cluster-randomized trial designs (single-year, 2-year parallel, and 2-year crossover) in the context of a 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria. Mixed-effects models, generalized estimating equations, and cluster-level analyses were evaluated. Additional 2-year parallel designs with different numbers of clusters and different cluster correlations were also explored. Results: Mixed-effects models with a small sample correction and unweighted cluster-level summaries yielded both high power and control of the Type I error rate. Generalized estimating equation approaches that utilized small sample corrections controlled the Type I error rate but did not confer greater power when compared to a mixed model approach with small sample correction. The crossover design generally yielded higher power relative to the parallel equivalent. Differences in power between analysis methods became less pronounced as the number of clusters increased. The strength of within-cluster correlation impacted the relative differences in power. Conclusion: Regardless of study design, cluster-level analyses as well as individual-level analyses like mixed-effects models or generalized estimating equations with small sample size corrections can both provide reliable results in small cluster settings. For 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria, we recommend a mixed-effects model with a pseudo-likelihood approximation method and Kenward–Roger correction. Similarly designed studies with small sample sizes and count outcomes should consider adjustments for small sample sizes when using a mixed-effects model or generalized estimating equation for analysis. Although the 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria is already underway as a parallel trial, applying the simulation parameters to a crossover design yielded improved power, suggesting that crossover designs may be valuable in settings where the number of available clusters is limited. Finally, the sensitivity of the analysis approach to the strength of within-cluster correlation should be carefully considered when selecting the primary analysis for a cluster-randomized trial.

Human faces are variable; we look different from one another. Craniofacial disorders further increase facial variation. To understand craniofacial variation and how it can be buffered, we analyzed the zebrafish mef2ca mutant. When this transcription factor encoding gene is mutated, zebrafish develop dramatically variable craniofacial phenotypes. Years of selective breeding for low and high penetrance of mutant phenotypes produced strains that are either resilient or sensitive to the mef2ca mutation. Here, we compared gene expression between these strains, which revealed that selective breeding enriched for high and low mef2ca paralog expression in the low- and high-penetrance strains, respectively. We found that mef2ca paralog expression is variable in unselected wild-type zebrafish, motivating the hypothesis that heritable variation in paralog expression underlies mutant phenotype severity and variation. In support, mutagenizing the mef2ca paralogs, mef2aa, mef2b, mef2cb, and mef2d demonstrated modular buffering by paralogs. Specifically, some paralogs buffer severity while others buffer variability. We present a novel, mechanistic model for phenotypic variation where variable, vestigial paralog expression buffers development. These studies are a major step forward in understanding the mechanisms of facial variation, including how some genetically resilient individuals can overcome a deleterious mutation.

ObjectiveWe aimed to evaluate the association between cumulative illness with neurodevelopment and growth outcomes in a birth cohort of Guatemalan infants.Study DesignFrom June 2017 to July 2018, infants 0-3 months of age living in a resource-limited region of rural southwest Guatemala were enrolled and completed weekly at-home surveillance for caregiver-reported cough, fever and vomiting/diarrhea. They also underwent anthropometric assessments and neurodevelopmental testing with the Mullen Scales of Early Learning (MSEL) at enrollment, six months, and one year.ResultsOut of 499 enrolled infants, 430 (86.2%) completed all study procedures and were included in the analysis. At 12-15 months of age, 140 (32.6%) infants had stunting (length-for-age Z [LAZ] score ConclusionsThese findings highlight the negative cumulative consequences of frequent febrile and respiratory illness on neurodevelopment during infancy. Future studies should explore the inflammatory profile associated with these syndromic illnesses and their impact on neurodevelopment in the first years of life.

Background Use of neoadjuvant therapy for elderly patients with pancreatic cancer has been debatable. With FOLFIRINOX (folinic acid, 5-fluorouracil, irinotecan, oxaliplatin) or gemcitabine plus nab-paclitaxel (GnP) showing tremendous effects in improving the overall survival of patients with borderline resectable and locally advanced pancreatic cancer, there is no definitive consensus regarding the use of this regimen in the elderly. Methods This study evaluated the eligibility of elderly patients with borderline resectable or locally advanced pancreatic cancer for neoadjuvant therapy. Patients registered in the database of pancreatic cancer at the University of Colorado Cancer Center, who underwent neoadjuvant treatment between January 2011 and March 2019, were separated into three age groups (less than 70, 70–74, 75 or more years) and respective treatment outcomes were compared. Results The study included 246 patients with pancreatic cancer who underwent neoadjuvant treatment, of whom 154 and 71 received chemotherapy with FOLFIRINOX and GnP respectively. Among these 225 patients, 155 were younger than 70 years, 36 were aged 70–74 years, and 34 were aged 75 years or older. Patients under 70 years old received FOLFIRINOX most frequently (124 of 155 versus 18 of 36 aged 70–74 years, and 12 of 34 aged 75 years or more; P Conclusion The safety and efficacy of multiagent chemotherapy in patients aged 75 years or over were similar to those in younger patients. Modern multiagent regimens could be a safe and viable treatment option for clinically fit patients aged at least 75 years.

Surgical site infections (SSI) following congenital heart surgery (CHS) remain a significant source of morbidity. Delayed sternal closure (DSC) is often required to minimize the potential for hemodynamic instability. The purpose of this study was to determine the incidence of SSI among patients undergoing DSC versus primary chest closure (PCC) and to define a potential inflection point for increased risk of SSI as a function of open chest duration (OCD).A retrospective review of our institutional Society of Thoracic Surgeons dataset is to identify patients undergoing CHS at our institution between 2015 and 2020. Incidences of SSI were compared between DSC and PCC patients. DSC patients were evaluated to determine the association of OCD and the incidence of SSI.2582 operations were performed at our institution between 2015 and 2020, including 195 DSC and 2387 PCC cases. The incidence of SSI within the cohort was 1.8% (47/2,582). DSC patients had significantly higher incidences of SSI (17/195, 8.7%) than PCC patients (30/2387, 1.3%, p 0.001). Further, patients with an OCD of four or more days had a significantly higher incidence of SSI (11/62, 17.7%, p = 0.006) than patients with an OCD less than 4 days (6/115, 5.3%).The incidence of SSI following CHS is higher in DSC patients compared to PCC patients. Prolonged OCD of 4 days or more significantly increases the risk of SSI and represents a potentially modifiable risk factor for SSI predisposition. These data support dedicated, daily post-operative assessment of candidacy for chest closure to minimize the risk of SSI.

Background Postoperative complications, length of index hospital stay, and unplanned hospital readmissions are important metrics reflecting surgical care quality. Postoperative infections represent a substantial proportion of all postoperative complications. We examined the relationships between identification of postoperative infection prehospital and posthospital discharge, length of stay, and unplanned readmissions in the American College of Surgeons National Surgical Quality Improvement Program database across nine surgical specialties. Methods The 30-day postoperative infectious complications including sepsis, surgical site infections, pneumonia, and urinary tract infection were analyzed in the American College of Surgeons National Surgical Quality Improvement Program inpatient data during the period from 2012 to 2017. General, gynecologic, vascular, orthopedic, otolaryngology, plastic, thoracic, urologic, and neurosurgical inpatient operations were selected. Results Postoperative infectious complications were identified in 5.2% (137,014/2,620,450) of cases; 81,929 (59.8%) were postdischarge. The percentage of specific complications identified postdischarge were 73.4% of surgical site infections (range across specialties 63.7–93.1%); 34.9% of sepsis cases (27.4–58.1%); 26.5% of pneumonia cases (18.9%–36.3%); and 53.2% of urinary tract infections (48.3%–88.0%). The relative risk of readmission among patients with postdischarge versus predischarge surgical site infection, sepsis, pneumonia, or urinary tract infection was 5.13 (95% confidence interval: 4.90–5.37), 9.63 (8.93–10.40), 10.79 (10.15–11.45), and 3.32 (3.07–3.60), respectively. Over time, mean length of stay decreased but postdischarge infections and readmission rates significantly increased. Conclusion Most postoperative infectious complications were diagnosed postdischarge. These were associated with an increased risk of readmission. The trend toward shorter length of stay over time was observed along with an increase both in the percentage of infections detected after discharge and the rate of unplanned related postoperative readmissions over time. Postoperative surveillance of infections should extend beyond hospital discharge of surgical patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated rapid local public health response, but studies examining the impact of social distancing policies on SARS-CoV-2 transmission have struggled to capture regional-level dynamics. We developed a susceptible-exposed-infected-recovered transmission model, parameterized to Colorado, USA‒specific data, to estimate the impact of coronavirus disease‒related policy measures on mobility and SARS-CoV-2 transmission in real time. During March‒June 2020, we estimated unknown parameter values and generated scenario-based projections of future clinical care needs. Early coronavirus disease policy measures, including a stay-at-home order, were accompanied by substantial decreases in mobility and reduced the effective reproductive number well below 1. When some restrictions were eased in late April, mobility increased to near baseline levels, but transmission remained low (effective reproductive number

ImportanceThe number of patients with small nonfunctional pancreatic neuroendocrine tumors (NF-PanNETs) is increasing. However, the role of surgery for small NF-PanNETs remains unclear.ObjectiveTo evaluate the association between surgical resection for NF-PanNETs measuring 2 cm or smaller and survival.Design, Setting, and ParticipantsThis cohort study used data from the National Cancer Database and included patients with NF-pancreatic neuroendocrine neoplasms who were diagnosed between January 1, 2004, and December 31, 2017. Patients with small NF-PanNETs were divided into 2 groups: group 1a (tumor size, ≤1 cm) and group 1b (tumor size, 1.1-2.0 cm). Patients without information on tumor size, overall survival, and surgical resection were excluded. Data analysis was performed in June 2022.ExposuresPatients with vs without surgical resection.Main Outcomes and MeasuresThe primary outcome was overall survival of patients in group 1a or group 1b who underwent surgical resection compared with those who did not, which was evaluated using Kaplan-Meier estimates and multivariable Cox proportional hazards regression models. Interactions between preoperative factors and surgical resection were analyzed with a multivariable Cox proportional hazards regression model.ResultsOf the 10 504 patients with localized NF-PanNETs identified, 4641 were analyzed. These patients had a mean (SD) age of 60.5 (12.7) years and included 2338 males (50.4%). The median (IQR) follow-up time was 47.1 (28.2-71.6) months. In total, 1278 patients were in group 1a and 3363 patients were in group 1b. The surgical resection rates were 82.0% in group 1a and 87.0% in group 1b. After adjustment for preoperative factors, surgical resection was associated with longer survival for patients in group 1b (hazard ratio [HR], 0.58; 95% CI, 0.42-0.80; P P = .12). In group 1b, interaction analysis found that age of 64 years or younger, absence of comorbidities, treatment at academic institutions, and distal pancreatic tumors were factors associated with increased survival after surgical resection.Conclusions and RelevanceFindings of this study support an association between surgical resection and increased survival in select patients with NF-PanNETs measuring 1.1 to 2.0 cm who were younger than 65 years, had no comorbidities, received treatment at academic institutions, and had tumors of the distal pancreas. Future investigations of surgical resection for small NF-PanNETs that include the Ki-67 index are warranted to validate these findings.

Objective To assess the contribution of unknown institutional factors (contextual effects) in the de-implementation of completion axillary node dissection (cALND) in women with breast cancer. Summary background data Women included in the National Cancer Database with invasive breast carcinoma from 2012-2016 that underwent upfront lumpectomy and were found to have a positive sentinel node. Methods A multivariable mixed effects logistic regression model with a random intercept for site was used to determine the effect of patient, tumor, and institutional variables on the risk of cALND. Reference Effect Measures (REM) were used to describe and compare the contribution of contextual effects to the variation in cALND use to that of measured variables. Results By 2016, cALND was still performed in at least 50% of the patients in a quarter of the institutions. Black race, younger women and those with larger or hormone negative tumors were more likely to undergo cALND. However, the width of the 90% REM range for the contextual effects exceeded that of the measured site, tumor, time, and patient demographics, suggesting institutional contextual effects were the major drivers of cALND de-implementation. For instance, a woman at an institution with low-risk of performing cALND would have 74% reduced odds of having a cALND than if she was treated at a median-risk institution, while a patient at a high-risk institution had 3.91 times the odds. Conclusion Compared to known patient, tumor and institutional factors, contextual effects had a higher contribution to the variation in cALND use.

The Screening and Linkage to Services for Autism pragmatic trial examined the effect of family navigation on referral and evaluation for autism spectrum disorder in an urban healthcare system serving low-income families. Children aged 16–30 months with initial scores ⩾3 on the Modified Checklist for Autism in Toddlers–Revised with Follow-up (M-CHAT-R/F) were randomized to usual care ( n = 133) or navigation if indicated ( n = 142). Bilingual, bicultural autism family navigators offered navigation to intervention-group families if the completed M-CHAT-R/F indicated the need for autism spectrum disorder diagnostic evaluation. Autism family navigators also assisted families to arrange for completion of the M-CHAT-R/F interview if not done, and then offered navigation if indicated. Of 49 families for whom navigation was indicated, 45% received navigation, including care coordination, education, and practical support. In intention-to-treat analyses, the intervention did not increase referrals for evaluation but more than doubled completed autism spectrum disorder diagnostic evaluations. Intervention children were also more likely to complete the M-CHAT-R/F interview with fidelity. Research is needed to identify effective methods to improve uptake of family navigation. Family navigation may be an important tool for reducing systemic inequalities in the early identification and treatment of young children with autism spectrum disorder ( ClinicalTrials.gov , https://clinicaltrials.gov/ ; NCT02374541).Lay abstractYoung children with autism spectrum disorder can benefit from early detection and intervention to moderate autism spectrum disorder’s effect on their development. Routine screening with standardized instruments facilitates early identification. However, many screen-positive children do not receive referrals for evaluation, complete diagnostic evaluations, or engage in services. This is especially true for children of color and those from less educated or low-income families. The Screening and Linkage to Services for Autism trial evaluated the effect of family navigation on referral and evaluation for autism spectrum disorder in screen-positive toddlers. The trial was conducted in an urban, low-income population. It was designed to test “real-world” implementation of family navigation. We randomly assigned 275 children aged 16–30 months with an initial positive autism spectrum disorder screen to receive navigation if indicated or usual care. Navigation increased the number of at-risk toddlers who were evaluated for autism spectrum disorder. It also improved the quality of autism spectrum disorder screening in primary care clinics. However, many children failed to receive indicated referrals for evaluation. Navigation did not improve referral rates. Furthermore, nearly half of families who were potentially eligible for navigation could not be contacted. Other ways of structuring the navigation program may be necessary to improve families’ access to and acceptance of navigation, especially in the setting of financial, cultural, and linguistic barriers. Our trial nevertheless showed that family navigation may be an important tool for reducing systemic inequalities in the early identification and treatment of young children with autism spectrum disorder.

Objectives To use electronic health record data from the first 2 hours of care to derive and validate a model to predict hypotensive septic shock in children with infection. Design Derivation-validation study using an existing registry. Setting Six emergency care sites within a regional pediatric healthcare system. Three datasets of unique visits were designated:1)training set (five sites, April 1, 2013, to December 31, 2016),2)temporal test set (five sites, January 1, 2017, to June 30, 2018), and3)geographic test set (sixth site, April 1, 2013, to June 30, 2018). Patients Patients in whom clinicians were concerned about serious infection from 60 days to 18 years were included; those with septic shock in the first 2 hours were excluded. There were 2,318 included visits; 197 developed septic shock (8.5%). Interventions Lasso with 10-fold cross-validation was used for variable selection; logistic regression was then used to construct a model from those variables in the training set. Variables were derived from electronic health record data known in the first 2 hours, including vital signs, medical history, demographics, and laboratory information. Test characteristics at two thresholds were evaluated: 1) optimizing sensitivity and specificity and 2) set to 90% sensitivity. Measurements and main results Septic shock was defined as systolic hypotension and vasoactive use or greater than or equal to 30 mL/kg isotonic crystalloid administration in the first 24 hours. A model was created using 20 predictors, with an area under the receiver operating curve in the training set of 0.85 (0.82-0.88); 0.83 (0.78-0.89) in the temporal test set and 0.83 (0.60-1.00) in the geographic test set. Sensitivity and specificity varied based on cutpoint; when sensitivity in the training set was set to 90% (83-94%), specificity was 62% (60-65%). Conclusions This model predicted risk of septic shock in children with suspected infection 2 hours after arrival, a critical timepoint for emergent treatment and transfer decisions. Varied cutpoints could be used to customize sensitivity to clinical context.

Background Neoadjuvant treatment is important for improving the rate of R0 surgical resection and overall survival outcome in treating patients with pancreatic ductal adenocarcinoma (PDAC). However, the true efficacy of radiotherapy (RT) for neoadjuvant treatment of PDAC is uncertain. This retrospective study evaluated the treatment outcome of neoadjuvant RT in the treatment of PDAC. Methods Collected from the National Cancer Database, information on patients with PDAC who underwent neoadjuvant chemotherapy (NAC) and pancreatectomy between 2010 to 2016 was used in this study. Short- and long-term outcomes were compared between patients who received neoadjuvant chemoradiotherapy (NACRT) and NAC. Results The study included 6936 patients, of whom 3185 received NACRT and 3751 NAC. The groups showed no difference in overall survival (NACRT 16.1 months versus NAC 17.4 months; P = 0.054). NACRT is associated with more frequent margin negative resection (86.1 versus 80.0 per cent; P < 0.001) but a more unfavourable 90-day mortality than NAC (6.4 versus 3.6 per cent; P < 0.001). The odds of 90-day mortality were higher in the radiotherapy group (odds ratio 1.81; P < 0.001), even after adjusting for significant covariates. Patients who received NACRT received single-agent chemotherapy more often than those who received NAC (31.5 versus 10.7 per cent; P < 0.001). Conclusion This study failed to show a survival benefit for NACRT over NAC alone, despite its association with negative margin resection. The significantly higher mortality in NACRT warrants further investigation into its efficacy in the treatment of pancreatic cancer.

Background: Formal surgical risk assessment tools have been developed to predict risk of adverse postoperative patient outcomes. Such tools accurately predict common postoperative complications, inform patients and providers of likely perioperative outcomes, guide decision making, and improve patient care. However, these are underutilized. We studied the attitudes towards and techniques of how surgeons preoperatively assess risk.Methods: 108 surgical faculty, 95 surgical residents, and 58 affiliate surgeons at a large academic tertiary referral hospital and affiliate community hospitals were emailed a 16-question survey between 8/2019-6/2020. Surveys were analyzed using descriptive statistics (frequency distributions and percentages for categorical variables, means, and standard deviations for continuous variables), and Fisher’s exact test and unpaired t-tests comparing responses by surgical attendings vs. residents.Results: Overall response rates were 50.0% for faculty surgeons, 47.4% for residents, and 36.2% for affiliate surgeons. Only 20.8% of surgeons used risk calculators most or all of the time. Attending surgeons were more likely to use prior experience and current literature while residents used risk calculators more frequently. Risk assessment tools were more likely to be used when predicting major complications and death in older patients with significant risk factors. Greatest barriers for use of risk assessment tools included time, inaccessibility, and trust in accuracy.Conclusions: Even though risk assessment tools are available, few surgeons use them as part of their routine practices. Developers of the tools need to design the tools to better promote the positive features and to avoid the burdens commonly reported by users.

We evaluated whether Denver neighborhoods with elevated rates of adult hospitalizations for laboratory-confirmed influenza had lower adult coverage with influenza vaccine. Overall vaccine coverage was low. Hospitalization rates were associated with demographic and socioeconomic characteristics. Active immunization of at-risk neighborhoods may be necessary to address disparities in influenza hospitalization rates.

Background Malaria is one of the greatest recurring threats to public health in Mozambique with approximately 10 million cases and thousands of deaths reported annually. Although a malaria early warning system is being established in the country, it is focused on short-term (4–8 week) prediction windows. Increased understanding of the links between quasi-predictable interannual climate variability and malaria could lengthen the lead time for the malaria early warning system and enhance planning and actionable mitigation efforts by public health officials in the country. Methods To identify patterns of interannual variability, we did two principal component analyses of processed weekly district-level malaria incidences for the period 2010–17 in Mozambique and southern Africa regional precipitation for the period 1981–2019. We also did linear regression analyses of sea surface temperatures onto a precipitation index, and composites of various climate variables to establish links between precipitation and modes of climate variability. Findings Two dominant spatiotemporal patterns collectively account for 81% of the interannual variability of malaria in Mozambique. These patterns consisted of a primary hotspot over the central and southern part of the country and a secondary hotspot over the northern third of the country. We found these patterns to be closely related to precipitation variability driven by climate phenomena: the El Nino-Southern Oscillation and the Subtropical Indian Ocean Dipole. Interpretation Quasi-predictable global and regional climate phenomena influence regional precipitation over southern Africa. Knowledge of these associations can inform an enhancement of the malaria early warning system in Mozambique by lengthening early warning lead times. Similar analyses are possible for other countries whose climate is dominated by tropical modes of climate variability. Funding None.

Initial treatment with either neoadjuvant chemoradiation (CRT) or induction FOLFOX (5-Fluorouracil, leucovorin, and oxaliplatin) chemotherapy followed by CRT is considered standard treatment for locally advanced rectal cancer. We compared patient-reported outcomes (PRO) during CRT in patients who had received induction chemotherapy versus those who did not.We reviewed records of patients with locally advanced rectal cancer who were treated with CRT between September 2009 and October 2014, and who had completed ≥ 4 PRO assessments during treatment. Clinician- and patient-reported toxicities were collected each week during treatment. We fit binomial generalized linear models to maximum toxicity scores across all patients' visits.Of 123 patients with ≥ 4 PRO assessments, 87 (71%) patients reported a clinically meaningful PRO score of 3 or higher for diarrhea, and 91 (74%) patients reported a PRO score of ≥ 3 for urgency, during 1 or more weeks of treatment, corresponding to 'very frequent' or worse. Of 116 patients who had also completed ≥ 4 clinician-reported assessments for descriptive analysis, clinically significant diarrhea (Common Terminology Criteria for Adverse Events grade ≥ 2) was reported in 9% of patients, and grade 2 proctitis and cystitis were reported in 20% and 4%, respectively. Eighty-four (68%) patients had undergone induction chemotherapy prior to CRT. Patients who received induction chemotherapy had 68% lower odds of experiencing significant urgency (odds ratio [OR], 0.32; 95% confidence interval [CI], 0.11-0.95; P = .04), 76% lower odds of bleeding (OR, 0.24; 95% CI, 0.1-0.62; P .01), and 75% lower odds of tenesmus (OR, 0.25; 95% CI, 0.11-0.6; P .01) versus those treated with upfront CRT.Based on PROs, a high proportion of patients experienced clinically significant symptoms during pelvic CRT, with diarrhea and urgency being most commonly reported. This appears to be under-reported on clinician-reported assessments. Delivery of induction chemotherapy was associated with lower odds of experiencing urgency, bleeding, and tenesmus on PROs during subsequent CRT, with no significant impact on diarrhea and rectal pain.

Background The Epstein-Barr virus (EBV) viral glycoprotein gp350 has been proposed as a candidate antigen for an EBV vaccine. However, the proposed formulations of these vaccines have not taken into account the presence of 2 unique EBV strains (EBV-1 and EBV-2) present in areas of high incidence of the EBV-associated cancer, Burkitt lymphoma. Methods In this study, we analyze the kinetics of EBV-1 and EBV-2 infection in an asymptomatic infant cohort from Kisumu, Kenya. We also analyzed the kinetics of the antibody response against 5 EBV antigens, gp350 (IgG and IgA), VCA (IgG), EBNA-1 (IgG), EAd (IgG), and Zta (IgG). Results We observed a high frequency of coinfection with both EBV types over time, with the only observable defect in the antibody response in infants coinfected being a significantly lower level of anti-gp350 IgA at peak response. Gp350 IgA levels were also significantly lower in coinfected infants 2.5 months postinfection and at the time of coinfection. Conclusions These results suggest that anti-gp350 IgA antibodies may be important for sterilizing immunity against secondary infection. These findings have implications for the development of an efficacious EBV vaccine to prevent both EBV-1 and EBV-2 infection in a population at high risk for Burkitt lymphoma.