Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D's Real-Time Capabilities and Cross-Hospital Transferability.

Background: Postoperative ileus (POI) after colorectal surgery leads to increased morbidity, costs, and hospital stays. Identifying POI risk for early intervention is important for improving surgical outcomes especially given the increasing trend towards early discharge after surgery. While existing studies have assessed POI risk with regression models, the role of deep learning's remains unexplored. Methods: We assessed the performance and transferability (brutal force/instance/parameter transfer) of Gated Recurrent Unit with Decay (GRU-D), a longitudinal deep learning architecture, for real-time risk assessment of POI among 7,349 colorectal surgeries performed across three hospital sites operated by Mayo Clinic with two electronic health records (EHR) systems. The results were compared with atemporal models on a panel of benchmark metrics. Results: GRU-D exhibits robust transferability across different EHR systems and hospital sites, showing enhanced performance by integrating new measurements, even amid the extreme sparsity of real-world longitudinal data. On average, for labs, vitals, and assisted living status, 72.2%, 26.9%, and 49.3% respectively lack measurements within 24 hours after surgery. Over the follow-up period with 4-hour intervals, 98.7%, 84%, and 95.8% of data points are missing, respectively. A maximum of 5% decrease in AUROC was observed in brutal-force transfer between different EHR systems with non-overlapping surgery date frames. Multi-source instance transfer witnessed the best performance, with a maximum of 2.6% improvement in AUROC over local learning. The significant benefit, however, lies in the reduction of variance (a maximum of 86% decrease). The GRU-D model's performance mainly depends on the prediction task's difficulty, especially the case prevalence rate. Whereas the impact of training data and transfer strategy is less crucial, underscoring the challenge of effectively leveraging transfer learning for rare outcomes. While atemporal Logit models show notably superior performance at certain pre-surgical points, their performance fluctuate significantly and generally underperform GRU-D in post-surgical hours. Conclusion: GRU-D demonstrated robust transferability across EHR systems and hospital sites with highly sparse real-world EHR data. Further research on built-in explainability for meaningful intervention would be highly valuable for its integration into clinical practice.

Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial.

BACKGROUND: As primary care populations age, timely identification of palliative care need is becoming increasingly relevant. Previous studies have targeted particular patient populations with life-limiting disease, but few have focused on patients in a primary care setting. Toward this end, we propose a stepped-wedge pragmatic randomized trial whereby a machine learning algorithm identifies patients empaneled to primary care units at Mayo Clinic (Rochester, Minnesota, United States) with high likelihood of palliative care need. METHODS: 42 care team units in 9 clusters were randomized to 7 wedges, each lasting 42 days. For care teams in treatment wedges, palliative care specialists review identified patients, making recommendations to primary care providers when appropriate. Care teams in control wedges receive palliative care under the standard of care. DISCUSSION: This pragmatic trial therefore integrates machine learning into clinical decision making, instead of simply reporting theoretical predictive performance. Such integration has the possibility to decrease time to palliative care, improving patient quality of life and symptom burden. TRIAL REGISTRATION: Clinicaltrials.gov NCT04604457 , restrospectively registered 10/26/2020. PROTOCOL: v0.5, dated 9/23/2020.

Effect of an Artificial Intelligence Decision Support Tool on Palliative Care Referral in Hospitalized Patients: A Randomized Clinical Trial.

CONTEXT: Palliative care services are commonly provided to hospitalized patients, but accurately predicting who needs them remains a challenge. OBJECTIVES: To assess the effectiveness on clinical outcomes of an artificial intelligence (AI)/machine learning (ML) decision support tool for predicting patient need for palliative care services in the hospital. METHODS: The study design was a pragmatic, cluster-randomized, stepped-wedge clinical trial in 12 nursing units at two hospitals over a 15-month period between August 19, 2019, and November 17, 2020. Eligible patients were randomly assigned to either a medical service consultation recommendation triggered by an AI/ML tool predicting the need for palliative care services or usual care. The primary outcome was palliative care consultation note. Secondary outcomes included: hospital readmissions, length of stay, transfer to intensive care and palliative care consultation note by unit. RESULTS: A total of 3183 patient hospitalizations were enrolled. Of eligible patients, A total of 2544 patients were randomized to the decision support tool (1212; 48%) and usual care (1332; 52%). Of these, 1717 patients (67%) were retained for analyses. Patients randomized to the intervention had a statistically significant higher incidence rate of palliative care consultation compared to the control group (IRR, 1.44 [95% CI, 1.11-1.92]). Exploratory evidence suggested that the decision support tool group reduced 60-day and 90-day hospital readmissions (OR, 0.75 [95% CI, 0.57, 0.97]) and (OR, 0.72 [95% CI, 0.55-0.93]) respectively. CONCLUSION: A decision support tool integrated into palliative care practice and leveraging AI/ML demonstrated an increased palliative care consultation rate among hospitalized patients and reductions in hospitalizations.

Real-time risk prediction of colorectal surgery-related post-surgical complications using GRU-D model.

BACKGROUND: Post-surgical complications (PSCs) have been an increasing concern for hospitals in light of Medicare penalties for 30-day readmissions. PSCs have become a target for quality improvement and benchmark for the healthcare system. Over half (60 %) of the deep or organ space surgical site infections are discovered after discharge, leading to a readmission. There has thus been a push to develop risk prediction models for targeted preventive interventions for PSCs. OBJECTIVE: We experiment several Gated Recurrent Unit with Decay (GRU-D) based deep learning architectures with various feature sampling schemes in predicting the risk of colorectal PSCs and compare with atemporal logistic regression models (logit). METHOD: We used electronic health record (EHR) data of 3,535 colorectal surgical patients involved in the national surgical quality improvement program (NSQIP) between 2006 and 2018. Single layer, stacked layer, and multimodal GRU-D models with sigmoid activation were used to develop risk prediction models. Area Under the Receiver Operating Characteristic curve (AUROC) was calculated by comparing predicted probability of developing complications versus truly observed PSCs (NSQIP adjudicated) within 30 days after surgery. We set up cross-validation and an independent held-out dataset for testing model performance consistency. RESULTS AND CONCLUSION: The primary contribution of our study is the formulation of a novel real-time PSC risk prediction task using GRU-D with demonstrated clinical utility. GRU-D outperforms the logit model in predicting wound and organ space infection and shows improved performance as additional data points become available. Logit model outperforms GRU-D before surgery for superficial infection and bleeding. For the same sampling scheme, there is no obvious advantage of complex architectures (stacked, multimodal) over single layer GRU-D. Obtaining more data points closer to the occurrence of PSCs is more important than using a more frequent sampling scheme in training GRU-D models. The fourth predicted risk quartile by single layer GRU-D contains 63 %, 59 %, and 66 % organ space infection cases, at 4 h before, 72 h after, and 168 h after surgery, respectively, suggesting its potential application as a bedside risk assessment tool.

Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled trial.

BACKGROUND: Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care. METHODS: To that aim, we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary's Hospital & Methodist Hospital both within Mayo Clinic Rochester in Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care, and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months of recruitment unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance. DISCUSSION: This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice while retaining scientific rigor. TRIAL REGISTRATION: ClinicalTrials.gov NCT03976297 . Registered on 6 June 2019, prior to trial start.

Improving the delivery of palliative care through predictive modeling and healthcare informatics.

OBJECTIVE: Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team. MATERIALS AND METHODS: Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient's corresponding care team. RESULTS: Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an "in-production" AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes. CONCLUSIONS: A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.

Pragmatic cluster randomized trial to evaluate effectiveness and implementation of enhanced EHR-facilitated cancer symptom control (E2C2).

BACKGROUND: The prevalence of inadequate symptom control among cancer patients is quite high despite the availability of definitive care guidelines and accurate and efficient assessment tools. METHODS: We will conduct a hybrid type 2 stepped wedge pragmatic cluster randomized clinical trial to evaluate a guideline-informed enhanced, electronic health record (EHR)-facilitated cancer symptom control (E2C2) care model. Teams of clinicians at five hospitals that care for patients with various cancers will be randomly assigned in steps to the E2C2 intervention. The E2C2 intervention will have two levels of care: level 1 will offer low-touch, automated self-management support for patients reporting moderate sleep disturbance, pain, anxiety, depression, and energy deficit symptoms or limitations in physical function (or both). Level 2 will offer nurse-managed collaborative care for patients reporting more intense (severe) symptoms or functional limitations (or both). By surveying and interviewing clinical staff, we will also evaluate whether the use of a multifaceted, evidence-based implementation strategy to support adoption and use of the E2C2 technologies improves patient and clinical outcomes. Finally, we will conduct a mixed methods evaluation to identify disparities in the adoption and implementation of the E2C2 intervention among elderly and rural-dwelling patients with cancer. DISCUSSION: The E2C2 intervention offers a pragmatic, scalable approach to delivering guideline-based symptom and function management for cancer patients. Since discrete EHR-imbedded algorithms drive defining aspects of the intervention, the approach can be efficiently disseminated and updated by specifying and modifying these centralized EHR algorithms. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03892967. Registered on 25 March 2019.

Regional and Temporal Trends in the Outcomes of Repairs for Acute Type A Aortic Dissections.

BACKGROUND: Little information exists regarding the use of arch operations for repair of acute type A aortic dissections (AADs) despite increasing interest in this strategy and its potential impact on outcomes. We aimed to determine the relationship between extent of aortic repair, US geographic regions, and outcome. METHODS: We queried The Society of Thoracic Surgeons database for patients who underwent AAD repair from January 1, 2004 to December 31, 2016 and grouped patients by ascending-only operations and operations involving the arch. RESULTS: We identified 25,462 patients (mean age, 59.8 ± 14.2; 66.7% men) who underwent AAD repair. Operations involving the ascending aorta only were performed in 54% of patients; 46% had repair additionally involving the arch. The 30-day mortality was 18.9% for patients who underwent ascending-only operations vs 19.8% for patients who underwent arch operations (P = .09). In multivariable analysis older age (P < .001), earlier year of operation (P < .001), diabetes mellitus (P < .001), severe chronic lung disease (P < .001), prior cerebrovascular disease (P < .001), and longer bypass time (P < .001) were independently associated with 30-day mortality. There was regional variation in 30-day mortality (P < .001), and incidence of arch repair varied from 38.6% to 52.6% in 9 geographic regions (P < .001). CONCLUSIONS: In this analysis of cardiac surgical practice in the United States, repair of AADs included a portion of the aortic arch in 46% of patients. Early mortality remained high throughout the current era regardless of extent of aortic resection. Regional variation in perioperative mortality may signal an opportunity for practice improvement.

Assessing the Safety of Overlapping Surgery at a Children's Hospital.

IMPORTANCE: Media reports have questioned the safety of overlapping surgical procedures, and national scrutiny has underscored the necessity of single-center evaluations of its safety; however, sample sizes are likely small. We compared the safety profiles of overlapping and nonoverlapping pediatric procedures at a single children's hospital and discussed methodological considerations of the evaluation. DATA AND DESIGN: Retrospective analysis of inpatient pediatric surgical procedures (January 2013 to September 2015) at a single pediatric referral center. Overlapping and nonoverlapping procedures were matched in an unbalanced manner (m:n) by procedure. Mixed models adjusting for Vizient-predicted risk, case-mix, and surgeon compared inpatient mortality and length of stay (LOS). RESULTS: Among 315 overlapping procedures, 256 (81.3%) were matched to 645 nonoverlapping procedures. There were 6 deaths in all. The adjusted odds ratio for mortality did not differ significantly between nonoverlapping and overlapping procedures (adjusted odds ratio = 0.94 vs overlapping; 95% CI, 0.02-48.5; P = 0.98). Wide confidence intervals were minimally improved with Bayesian methods (95% CI, 0.07-12.5). Adjusted LOS estimates were not clinically different by overlapping status (0.6% longer for nonoverlapping; 95% CI, 9.7% shorter to 12.2% longer; P = 0.91). Among the 87 overlapping procedures with the greatest overlap (≥60 min or ≥50% of operative duration), there were no deaths. CONCLUSIONS: The safety of overlapping and nonoverlapping surgical procedures did not differ at this children's center. These findings may not extrapolate to other centers. LOS or intraoperative measures may be more appropriate than mortality for safety evaluations due to low event rates for mortality.

Incorporation of Treatment Response, Tumor Grade and Receptor Status Improves Staging Quality in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy.

BACKGROUND: Improved staging systems that better predict survival for breast cancer patients who receive neoadjuvant chemotherapy (NAC) by accounting for clinical pathological stage plus estrogen receptor (ER) and grade (CPS+EG) and ERBB2 status (Neo-Bioscore) have been proposed. We sought to evaluate the generalizability and performance of these staging systems in a national cohort. METHODS: The National Cancer Database (2006-2012) was reviewed for patients with breast cancer who received NAC and survived ≥90 days after surgery. Four systems were evaluated: clinical/pathologic American Joint Committee on Cancer (AJCC) 7th edition, CPS+EG, and Neo-Bioscore. Unadjusted Kaplan-Meier analysis and adjusted Cox proportional hazards models quantified overall survival (OS). Systems were compared using area under the curve (AUC) and integrated discrimination improvement (IDI). RESULTS: Overall, 43,320 patients (5-year OS 76.0, 95% confidence interval [CI] 75.4-76.5%) were included, 12,002 of whom had evaluable Neo-Bioscore. AUC at 5 years for CPS+EG (0.720, 95% CI 0.714-0.726) and Neo-Bioscore (0.729, 95% CI 0.716-0.742) were improved relative to AJCC clinical (0.650, 95% CI 0.643-0.656) and pathologic (0.683, 95% CI 0.676-0.689) staging. Both CPS+EG (IDI 7.2, 95% CI 6.6-7.7%) and Neo-Bioscore (IDI 9.8, 95% CI 8.0-11.6%) demonstrated superior discrimination when compared with AJCC clinical staging at 5 years. Comparison of CPS+EG with Neo-Bioscore yielded an IDI of 2.6% (95% CI 0.9-4.5%), indicating that Neo-Bioscore is the best staging system. CONCLUSIONS: In a heterogenous national cohort of breast cancer patients treated with NAC and surgery, the incorporation of chemotherapy response, tumor grade, ER status, and ERBB2 status into the staging system substantially improved on the AJCC TNM staging system in discrimination of OS. Neo-Bioscore provided the best staging discrimination.

Measuring the impact of ambulatory red blood cell transfusion on home functional status: study protocol for a pilot randomized controlled trial.

BACKGROUND: Red blood cell (RBC) transfusion is frequently employed in both ambulatory and hospital environments with the aim of improving patient functional status. In the ambulatory setting, this practice is particularly common in patients with malignancy due to anemia associated with their cancer therapy. Increasingly, the efficacy of this US$10.5 billion per year practice has been called into question. While it is often standard of care for patients with chemotherapy-induced anemia to receive ambulatory RBC transfusions, it is unclear to what extent such transfusions affect home functional status. It is also unclear whether or not changes in functional status in this population can be objectively quantified using wearable activity monitors. We propose to directly measure the impact of outpatient RBC transfusions on at-home functional status by recording several physiological parameters and quantifiable physical activity metrics, e.g., daily energy expenditure and daily total step count, using the ActiGraph wGT3X-BT. This device is an accelerometer-based wearable activity monitor similar in size to a small watch and is worn at the waist. Study participants will wear the device during the course of their daily activities giving us quantifiable insight into activity levels in the home environment. METHODS/DESIGN: This will be a randomized crossover pilot clinical trial with a participant study duration of 28 days. The crossover nature allows each patient to serve as their own control. Briefly, patients presenting at a tertiary medical center's Ambulatory Infusion Center (AIC) will be randomized to either: (1) receive an RBC transfusion as scheduled (transfusion) or (2) abstain from the scheduled transfusion (no transfusion). After an appropriate washout period, participants will crossover from the transfusion arm to the no-transfusion arm or vice versa. Activity levels will be recorded continuously throughout the study using an accelerometry monitor. In addition to device data, functional status and health outcomes will be collected via a weekly telephone interview. The primary outcome measure will be daily energy expenditure. Performance metrics, such as step count changes, will also be evaluated. Additional secondary outcome measures will include daily sedentary time and Patient-reported Outcomes Measurement Information System (PROMIS) Global 10 Survey scores. DISCUSSION: This trial will provide important information on the feasibility and utility of using accelerometry monitors to directly assess the impact of RBC transfusion on patients' functional status. The results of the study will inform the merit and methods of a more definitive future trial evaluating the impact of ambulatory RBC transfusions in the target population. TRIAL REGISTRATION: ClinicalTrials.gov, identifier: NCT02835937 . Registered on 15 July 2016.

Incidence and Management of Late Adverse Radiation Effects After Arteriovenous Malformation Radiosurgery.

BACKGROUND: Late adverse radiation effects (ARE) typically occur many years after stereotactic radiosurgery (SRS) of intracranial arteriovenous malformations (AVM). They are characterized by perilesional edema or cyst formation and are distinct from radiation-induced changes (RIC) noted in the first 1 to 2 years after AVM SRS and radiation necrosis. OBJECTIVE: To determine the incidence of late ARE after AVM SRS. METHODS: Retrospective review of 233 AVM patients having SRS from 1990 to 2009. Patients had sporadic AVM, no prior radiation, and a minimum of 5 years of magnetic resonance imaging (MRI) follow-up. The median MRI follow-up after SRS was 9.8 years (range, 5-24.2). RESULTS: Late ARE were observed in 16 patients (6.9%) at a median of 8.7 years after SRS (range, 2.0-16.1). The 5-, 10-, and 15-year incidence of late ARE was 0.4%, 7.7%, and 12.5%, respectively. Eight patients (3.4%) were symptomatic at the time of ARE detection. Three of 8 patients who were initially asymptomatic had documented cyst progression (at 11, 40, and 42 months), for an overall symptomatic rate of 4.7%. Five patients with asymptomatic ARE have been observed for a median of 9.3 years (range, 2.0-14.1) without progression. Patients having early RIC (hazard ratio [HR] = 2.11, P < .001), patients having obliteration (HR = 1.24, P = .02), and patients having SRS before April 1997 (HR = 1.12, P = .02) were more likely to develop late ARE. CONCLUSION: Late ARE are common in AVM patients who develop early RIC after SRS. Resection of the thrombosed AVM and the adjacent damaged tissue is effective at eliminating the mass effect and improving patients' neurological condition.

Identification of Clinically Meaningful Plasma Transfusion Subgroups Using Unsupervised Random Forest Clustering.

Statistical techniques such as propensity score matching and instrumental variable are commonly employed to "simulate" randomization and adjust for measured confounders in comparative effectiveness research. Despite such adjustments, the results of these methods apply essentially to an "average" patient. However, as patients show significant heterogeneity in their responses to treatments, this average effect is of limited value. It does not account for individual level variabilities, which can deviate substantially from the population average. To address this critical problem, we present a framework that allows the discovery of clinically meaningful homogeneous subgroups with differential effects of plasma transfusion using unsupervised random forest clustering. Subgroup analysis using two blood transfusion datasets show that considerable variablilities exist between the subgroups and population in both the treatment effect of plasma transfusion on bleeding and mortality and risk factors for these outcomes. These results support the customization of blood transfusion therapy for the individual patient.

Comparative analysis of arteriovenous malformation grading scales in predicting outcomes after stereotactic radiosurgery.

OBJECTIVE Successful stereotactic radiosurgery (SRS) for the treatment of arteriovenous malformations (AVMs) results in nidus obliteration without new neurological deficits related to either intracranial hemorrhage (ICH) or radiation-induced complications (RICs). In this study the authors compared 5 AVM grading scales (Spetzler-Martin grading scale, radiosurgery-based AVM score [RBAS], Heidelberg score, Virginia Radiosurgery AVM Scale [VRAS], and proton radiosurgery AVM scale [PRAS]) at predicting outcomes after SRS. METHODS The study group consisted of 381 patients with sporadic AVMs who underwent Gamma Knife SRS between January 1990 and December 2009; none of the patients underwent prior radiation therapy. The primary end point was AVM obliteration without a decline in modified Rankin Scale (mRS) score (excellent outcome). Comparison of the area under the receiver operating characteristic curve (AUC) and accuracy was performed between the AVM grading scales and the best linear regression model (generalized linear model, elastic net [GLMnet]). RESULTS The median radiological follow-up after initial SRS was 77 months; the median clinical follow-up was 93 months. AVM obliteration was documented in 297 patients (78.0%). Obliteration was 59% at 4 years and 85% at 8 years. Fifty-five patients (14.4%) had a decline in mRS score secondary to RICs (n = 29, 7.6%) or ICH (n = 26, 6.8%). The mRS score declined by 10% at 4 years and 15% at 8 years. Overall, 274 patients (71.9%) had excellent outcomes. There was no difference between the AUC for the GLMnet (0.69 [95% CI 0.64-0.75]), RBAS (0.68 [95% CI 0.62-0.74]), or PRAS (0.69 [95% CI 0.62-0.74]). Pairwise comparison for accuracy showed no difference between the GLMnet and the RBAS (p = 0.08) or PRAS (p = 0.16), but it did show a significant difference between the GLMnet and the Spetzler-Martin grading system (p < 0.001), Heidelberg score (p < 0.001), and the VRAS (p < 0.001). The RBAS and the PRAS were more accurate when compared with the Spetzler-Martin grading scale (p = 0.03 and p = 0.01), Heidelberg score (p = 0.02 and p = 0.02), and VRAS (p = 0.03 and p = 0.02). CONCLUSIONS SRS provides AVM obliteration without functional decline in the majority of treated patients. AVM grading scales having continuous scores (RBAS and PRAS) outperformed integer-based grading systems in the prediction of AVM obliteration without mRS score decline after SRS.

Safety of Overlapping Surgery at a High-volume Referral Center.

OBJECTIVE: To compare safety profiles of overlapping and nonoverlapping surgical procedures at a large tertiary-referral center where overlapping surgery is performed. BACKGROUND: Surgical procedures are frequently performed as overlapping, wherein one surgeon is responsible for 2 procedures occurring at the same time, but critical portions are not coincident. The safety of this practice has not been characterized. METHODS: Primary analyses included elective, adult, inpatient surgical procedures from January 2013 to September 2015 available through University HealthSystem Consortium. Overlapping and nonoverlapping procedures were matched in an unbalanced manner (m:n) by procedure type. Confirmatory analyses from the American College of Surgeons-National Surgical Quality Improvement Program investigated elective surgical procedures from January 2011 to December 2014. We compared outcomes mortality and length of stay after adjustment for registry-predicted risk, case-mix, and surgeon using mixed models. RESULTS: The University HealthSystem Consortium sample included 10,765 overlapping cases, of which 10,614 (98.6%) were matched to 16,111 nonoverlapping procedures. Adjusted odds ratio for inpatient mortality was greater for nonoverlapping procedures (adjusted odds ratio, OR = 2.14 vs overlapping procedures; 95% confidence interval, CI 1.23-3.73; P = 0.007) and length of stay was no different (+1% for nonoverlapping cases; 95% CI, -1% to +2%; P = 0.50). In confirmatory analyses, 93.7% (3712/3961) of overlapping procedures matched to 5,637 nonoverlapping procedures. The 30-day mortality (adjusted OR = 0.69 nonoverlapping vs overlapping procedures; 95% CI, 0.13-3.57; P = 0.65), morbidity (adjusted OR = 1.11; 95% CI, 0.92-1.35; P = 0.27) and length of stay (-4% for nonoverlapping; 95% CI, -4% to -3%; P < 0.001) were not clinically different. CONCLUSIONS: These findings from administrative and clinical registries support the safety of overlapping surgical procedures at this center but may not extrapolate to other centers.

Implications of CA19-9 elevation for survival, staging, and treatment sequencing in intrahepatic cholangiocarcinoma: A national cohort analysis.

BACKGROUND: Optimal management of patients with intrahepatic cholangiocarcinoma (ICCA) and elevated CA19-9 remains undefined. We hypothesized CA19-9 elevation above normal indicates aggressive biology and that inclusion of CA19-9 would improve staging discrimination. METHODS: The National Cancer Data Base (NCDB-2010-2012) was reviewed for patients with ICCA and reported CA19-9. Patients were stratified by CA19-9 above/below normal reference range. Unadjusted Kaplan-Meier and adjusted Cox-proportional-hazards analysis of overall survival (OS) were performed. RESULTS: A total of 2,816 patients were included: 938 (33.3%) normal; 1,878 (66.7%) elevated CA19-9 levels. Demographic/pathologic and chemotherapy/radiation were similar between groups, but patients with elevated CA19-9 had more nodal metastases and less likely to undergo resection. Among elevated-CA19-9 patients, stage-specific survival was decreased in all stages. Resected patients with CA19-9 elevation had similar peri-operative outcomes but decreased long-term survival. In adjusted analysis, CA19-9 elevation independently predicted increased mortality with impact similar to node-positivity, positive-margin resection, and non-receipt of chemotherapy. Proposed staging system including CA19-9 improved survival discrimination over AJCC 7th edition. CONCLUSION: Elevated CA19-9 is an independent risk factor for mortality in ICCA similar in impact to nodal metastases and positive resection margins. Inclusion of CA19-9 in a proposed staging system increases discrimination. Multi-disciplinary therapy should be considered in patients with ICCA and CA19-9 elevation. J. Surg. Oncol. 2016;114:475-482. © 2016 Wiley Periodicals, Inc.