Evaluating the ACS-NSQIP Risk Calculator in Primary GI Neuroendocrine Tumor: Results from the United States Neuroendocrine Tumor Study Group

2019 ◽  
Vol 85 (12) ◽  
pp. 1334-1340 ◽  
Author(s):  
Emily A. Armstrong ◽  
Eliza W. Beal ◽  
Alexandra G. Lopez-Aguiar ◽  
George Poultsides ◽  
John G. Cannon ◽  
...  
Keyword(s):  
Neuroendocrine Tumor ◽  
Area Under The Curve ◽  
The United States ◽  
Cardiac Complication ◽  
Patient Specific ◽  
Similar Proportion ◽  
Risk Calculator ◽  
Class Iii ◽  
Curative Intent ◽  
Acs Nsqip

The ACS established an online risk calculator to help surgeons make patient-specific estimates of postoperative morbidity and mortality. Our objective was to assess the accuracy of the ACS-NSQIP calculator for estimating risk after curative intent resection for primary GI neuroendocrine tumors (GI-NETs). Adult patients with GI-NET who underwent complete resection from 2000 to 2017 were identified using a multi-institutional database, including data from eight academic medical centers. The ability of the NSQIP calculator to accurately predict a particular outcome was assessed using receiver operating characteristic curves and the area under the curve (AUC). Seven hundred three patients were identified who met inclusion criteria. The most commonly performed procedures were resection of the small intestine with anastomosis (N = 193, 26%) and partial colectomy with anastomosis (N = 136, 18%). The majority of patients were younger than 65 years (N = 482, 37%) and ASA Class III (N = 337, 48%). The most common comorbidities were diabetes (N = 128, 18%) and hypertension (N = 395, 56%). Complications among these patients based on ACS NSQIP definitions included any complication (N = 132, 19%), serious complication (N = 118, 17%), pneumonia (N = 7, 1.0%), cardiac complication (N = 1, 0.01%), SSI (N = 80, 11.4%), UTI (N = 17, 2.4%), venous thromboembolism (N = 18, 2.5%), renal failure (N = 16, 2.3%), return to the operating room (N = 27, 3.8%), discharge to nursing/rehabilitation (N = 22, 3.1%), and 30-day mortality (N = 9, 1.3%). The calculator provided reasonable estimates of risk for pneumonia (AUC = 0.721), cardiac complication (AUC = 0.773), UTI (AUC = 0.716), and discharge to nursing/ rehabilitation (AUC = 0.779) and performed poorly (AUC < 0.7) for all other complications Fig. 1). The ACS-NSQIP risk calculator estimates a similar proportion of risk to actual events in patients with GI-NET but has low specificity for identifying the correct patients for many types of complications. The risk calculator may require modification for some patient populations.

An AUC Target Simulation for Vancomycin in Patients With Class III Obesity

2019 ◽  
pp. 089719001988524
Author(s):  
Meagan M. Langton ◽  
John W. Ahern ◽  
Julie MacDougall
Keyword(s):  
Area Under The Curve ◽  
Patient Specific ◽  
Pharmacokinetic Parameters ◽  
Therapeutic Drug ◽  
Target Area ◽  
Class Iii ◽  
Time Period ◽  
Class Iii Obesity ◽  
The University ◽  
University Of Vermont

Objective: The objective of this simulation is to compare 24-hour vancomycin (Vanc24) dosage requirements between a target area under the curve (AUC) versus a target trough approach in patients with class III obesity. Methods: Adult patients were included if they received vancomycin in accordance with the University of Vermont Medical Center’s class III obesity dosage protocol from June 2016 through December 2018. Patient-specific pharmacokinetic parameters were calculated for each patient using the Sawchuck-Zaske method. For this simulation, Vanc24 dosages were calculated to achieve an AUC of 400 mg/L h and a trough concentration of 15 mg/L. Results: Sixty-three patients had Vanc24 dosage requirements calculated. The median age was 59 years (interquartile range [IQR]: 51.5-68), body mass index (BMI): 45.7 kg/m2 (IQR: 42.4-51.5), and 50.7% were male. The mean Vanc24 dosage requirements were 3995 mg (standard deviation [SD] ±1673) in the target trough approach versus 2783 mg (SD ±1149) in the target AUC approach ( P < .0001). Conclusion: A target AUC approach required less vancomycin over a 24-hour time period relative to a target trough approach. Vancomycin therapeutic drug monitoring that explicitly targets AUC may reduce vancomycin exposure and potentially decrease the risk of nephrotoxicity in patients with class III obesity.

KOC (K homology domain containing protein overexpressed in cancer) overexpression and progression-free survival after curative intent resection of pancreatic ductal adenocarcinoma.

2012 ◽  
Vol 30 (30_suppl) ◽  
pp. 48-48
Author(s):  
Benny Johnson ◽  
Maged F. Khalil ◽  
Fan Lin ◽  
Shaobo Zhu ◽  
Lester Kirchner
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Distant Metastasis ◽  
Rna Binding ◽  
Progression Free Survival ◽  
The United States ◽  
Ductal Adenocarcinoma ◽  
Patient Specific ◽  
Rank Test ◽  
Curative Intent ◽  
Free Survival

48 Background: Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer mortality in the United States. Unfortunately, effective screening and early detection mechanisms are currently unavailable, thereby 80% of patients present with distant metastasis. Of the subset of patients eligible for curative intent surgery, the 5-year survival rate is only 20%. Negative surgical margins, tumor size, stage, and node negative disease are traditional prognostic indicators. However, these can be limited in their ability to predict patient specific prognosis. KOC is an oncofetal RNA-binding protein involved in RNA stabilization and cell growth during embryogenesis. Previous studies have revealed that KOC mRNA is inappropriately overexpressed in pancreatic cancer and that increased expression correlates with tumor stage. In this study, we attempt to identify whether KOC expression in patients who undergo curative intent surgery correlates with progression free survival. Methods: Tissue microarrays prepared from formalin-fixed, paraffin-embedded specimens of patients with PDAC who underwent curative intent surgery were assessed by immunohistochemistry. Results: A total of 35 patients were included. Comparisons between groups on progression free survival are estimated using the Kaplan-Meier method and the log-rank test. KOC was overexpressed in 23.6% of tumors. It was found that there were zero patients with a high KOC expression and no distant metastasis. Patients with a high KOC expression were more than 3 times more likely to progress compared to those with a low KOC expression (HR=3.54, 95% CI: 1.34, 9.36, p=0.011). Conclusions: KOC is a useful prognostic biomarker for predicting those patients with PDAC who have a high risk for early progression and distant metastasis. Identifying patients with high KOC expression upon initial diagnosis can serve as a way to risk stratify patients to aggressive treatment regimens upfront and early exposure to clinical trials.

Investigating the prognostic value of a panel of biomarkers after curative intent resection of pancreatic ductal adenocarcinoma.

2013 ◽  
Vol 31 (4_suppl) ◽  
pp. 211-211
Author(s):  
Benny Johnson ◽  
Maged F. Khalil ◽  
Joseph Blansfield ◽  
Fan Lin ◽  
Shaobo Zhu ◽  
...  
Keyword(s):  
Distant Metastasis ◽  
Expression Patterns ◽  
Progression Free Survival ◽  
The United States ◽  
Ductal Adenocarcinoma ◽  
Patient Specific ◽  
Rank Test ◽  
Ki 67 ◽  
Curative Intent ◽  
Free Survival

211 Background: Pancreatic adenocarcinoma (PDAC) is the fourth leading cause of cancer mortality in the United States. 80% of tumors are discovered with distant metastasis upon presentation. Of patients eligible for curative intent surgery, the 5-year survival rate is only 20%. Identification of a panel of biomarkers correlated with patient specific prognosis upon diagnosis can serve as a way to individualize treatment options. Methods: A retrospective cohort study analyzing pathology of patients who underwent curative intent surgery at our institution from 1998-2011 to identify whether the expression patterns of six biomarkers:S100P, Maspin, KOC, CEA, p53, and Ki-67 can be predicative of patient specific prognosis. Tissue microarrays of specimens were assessed by immunohistochemistry. Results: A total of 62 patients are included. Comparisons between groups on overall survival (OS) and progression free survival (PFS) are estimated using the Kaplan-Meier method and the log-rank test. Each biomarker was represented as low and high expression by categorizing the expression score at >4, based on intensity and extent of cells stained. 40 deaths occurred in the sample. Distant metastasis and differentiation (well/moderate vs. poor) were borderline related to OS (p=0.0120, p=0.0086). Interestingly, patients with a poor differentiation were less likely to die due to any cause (HR=0.41, 95% CI: 0.21, 0.82). 29 patients progressed in their disease. High/low KOC expression were significantly related to progression free survival (p=0.0556). Incorporating previously reported data on KOC, patients with a high KOC expression were more than 2 times more likely to progress compared to those with a low KOC expression (HR=2.04, 95% CI: 0.97, 4.29). Conclusions: In our study S100P, Maspin, CEA, p53 and Ki-67 expression patterns were not statistically significant in identifying PFS or OS in PDAC patients. However, our data is suggestive of KOC being a useful prognostic biomarker for identifying those patients with PDAC who have a high risk for early progression and distant metastasis. Larger studies are needed to determine whether KOC can be a therapeutic target in the treatment of pancreatic cancer.

Evaluating the ACS NSQIP Risk Calculator in Primary Pancreatic Neuroendocrine Tumor: Results from the US Neuroendocrine Tumor Study Group

2019 ◽  
Vol 23 (11) ◽  
pp. 2225-2231 ◽  
Author(s):  
Apeksha Dave ◽  
Eliza W. Beal ◽  
Alexandra G. Lopez-Aguiar ◽  
George Poultsides ◽  
Eleftherios Makris ◽  
...  
Keyword(s):  
Neuroendocrine Tumor ◽  
Pancreatic Neuroendocrine Tumor ◽  
Study Group ◽  
Risk Calculator ◽  
Acs Nsqip ◽  
Tumor Study ◽  
The Us ◽  
Tumor Study Group

Clinical benefit of breakthrough cancer drugs approved by the United States Food and Drug Administration.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 6513-6513
Author(s):  
Consolacion Molto ◽  
Thomas J. Hwang ◽  
Marta Andres ◽  
Maria Borrell ◽  
Ignasi J. Gich Saladich ◽  
...  
Keyword(s):  
Cancer Research ◽  
Clinical Benefit ◽  
The United States ◽  
Cancer Drug ◽  
Similar Proportion ◽  
Cancer Drugs ◽  
Open Label ◽  
Research Committee ◽  
Curative Intent ◽  
Therapy Program

6513 Background: The Breakthrough Therapy program was established in July 2012 to expedite drug development and approval by the FDA. We compared the characteristics of clinical trials leading to FDA approval as well as the magnitude of clinical benefit and value framework scores of breakthrough-designated and non-breakthrough-designated cancer drugs. Methods: We searched the Drugs@FDA website for cancer drug approvals from July 2012 and December 2017. For each indication, we applied the value frameworks and used thresholds of high clinical benefit developed by American Society of Clinical Oncology Value Framework version 2 (ASCO VF v2; scores ≥45), the ASCO Cancer Research Committee (OS gains ≥2.5 months PFS gains ≥3 months), the European Society for Medical Oncology-Magnitude of Clinical Benefit Scale version 1.1 (ESMO-MCBS v1.1; grade of A or B for trials of curative intent and 4 or 5 for those of non-curative intent), and the National Comprehensive Cancer Network (NCCN) Evidence Blocks (scores of 4 and 5). Trial characteristics and value framework scores were compared using Chi squared or Mann Whitney U tests. Results: We identified 106 pivotal trials supporting the approval of 52 individual drugs for 96 indications. Of these indications, 38 (40%) received breakthrough designation. Compared with trials for non-breakthrough drugs (n = 62), trials for breakthrough drugs (n = 44) had smaller sample size (median 373 vs 612, P= .03), were less often randomized (57% vs 86%; P= .001) and more likely to be open-label (84% vs 53%, P= .001). Trials for breakthrough drugs were more likely to demonstrate high clinical benefit using ASCO VF (68% vs 31%, P= .002) and NCCN Evidence Blocks (86% vs 56%, P= .002). A similar proportion of trials supporting breakthrough and non-breakthrough drugs demonstrated high clinical benefit using the ASCO Cancer Research Committee (82% vs 68%, P= .25) and ESMO-MCBS (35% vs 33%; P= .87) frameworks. Conclusions: In patients with advanced solid tumors, cancer drugs approved under breakthrough therapy designation were more likely to demonstrate high clinical benefit as defined by the ASCO VF and NCCN value frameworks. A similar proportion of approved breakthrough and non-breakthrough therapy drugs met the high benefit thresholds using the ASCO Cancer Research Committee and ESMO-MCBS frameworks.

Correlation of perioperative risk scores with hospital costs in neurosurgical patients

2020 ◽  
Vol 132 (3) ◽  
pp. 818-824
Author(s):  
Sasha Vaziri ◽  
Joseph M. Abbatematteo ◽  
Max S. Fleisher ◽  
Alexander B. Dru ◽  
Dennis T. Lockney ◽  
...  
Keyword(s):  
Surgical Site Infection ◽  
Hospital Costs ◽  
Patient Characteristics ◽  
Risk Scores ◽  
Site Infection ◽  
Risk Calculator ◽  
Surgical Risk ◽  
Acs Nsqip ◽  
Neurosurgical Patients ◽  
Predicted Risk

OBJECTIVEThe American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) online surgical risk calculator uses inherent patient characteristics to provide predictive risk scores for adverse postoperative events. The purpose of this study was to determine if predicted perioperative risk scores correlate with actual hospital costs.METHODSA single-center retrospective review of 1005 neurosurgical patients treated between September 1, 2011, and December 31, 2014, was performed. Individual patient characteristics were entered into the NSQIP calculator. Predicted risk scores were compared with actual in-hospital costs obtained from a billing database. Correlational statistics were used to determine if patients with higher risk scores were associated with increased in-hospital costs.RESULTSThe Pearson correlation coefficient (R) was used to assess the correlation between 11 types of predicted complication risk scores and 5 types of encounter costs from 1005 health encounters involving neurosurgical procedures. Risk scores in categories such as any complication, serious complication, pneumonia, cardiac complication, surgical site infection, urinary tract infection, venous thromboembolism, renal failure, return to operating room, death, and discharge to nursing home or rehabilitation facility were obtained. Patients with higher predicted risk scores in all measures except surgical site infection were found to have a statistically significant association with increased actual in-hospital costs (p < 0.0005).CONCLUSIONSPrevious work has demonstrated that the ACS NSQIP surgical risk calculator can accurately predict mortality after neurosurgery but is poorly predictive of other potential adverse events and clinical outcomes. However, this study demonstrates that predicted high-risk patients identified by the ACS NSQIP surgical risk calculator have a statistically significant moderate correlation to increased actual in-hospital costs. The NSQIP calculator may not accurately predict the occurrence of surgical complications (as demonstrated previously), but future iterations of the ACS universal risk calculator may be effective in predicting actual in-hospital costs, which could be advantageous in the current value-based healthcare environment.

scholarly journals Interventional Oncology Approach to Hepatic Metastases

2020 ◽  
Vol 37 (05) ◽  
pp. 484-491
Author(s):  
Cathal O'Leary ◽  
Michael C. Soulen ◽  
Susan Shamimi-Noori
Keyword(s):  
Liver Disease ◽  
Liver Function ◽  
Interventional Oncology ◽  
Hepatic Metastases ◽  
Locoregional Therapy ◽  
Treatment Modalities ◽  
Patient Specific ◽  
Curative Intent ◽  
Locoregional Therapies ◽  
Metastatic Liver

AbstractMetastatic liver disease is one of the major causes of cancer-related morbidity and mortality. Locoregional therapies offered by interventional oncologists alleviate cancer-related morbidity and in some cases improve survival. Locoregional therapies are often palliative in nature but occasionally can be used with curative intent. This review will discuss important factors to consider prior to palliative and curative intent treatment of metastatic liver disease with locoregional therapy. These factors include those specific to the tumor, liver function, liver reserve, differences between treatment modalities, and patient-specific considerations.

scholarly journals Neurocognitive effects associated with proprotein convertase subtilisin-kexin type 9 inhibitor use: a narrative review

2021 ◽  
Vol 12 ◽  
pp. 204209862095927
Author(s):  
Wei C. Yuet ◽  
Didi Ebert ◽  
Michael Jann
Keyword(s):  
Cognitive Impairment ◽  
Adverse Events ◽  
The United States ◽  
Narrative Review ◽  
Lipid Lowering ◽  
Patient Specific ◽  
Proprotein Convertase ◽  
Pcsk9 Inhibitors ◽  
Pcsk9 Inhibitor ◽  
Receptor Modulation

Neurocognitive adverse events have been observed with the widespread use of 3-hydroxy-3-methylglutaryl-CoA reductase inhibitors or “statins,” which reduce low-density lipoprotein cholesterol (LDL-C) levels and subsequently cardiovascular risk. The United States Food and Drug Association directed manufacturers of proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors to monitor for neurocognitive adverse events due to their potent effects on LDL-C reduction, which is a proposed mechanism for neuronal cell dysfunction. Other proposed mechanisms for PCSK9 inhibitor-associated neurocognitive adverse events include N-methyl-d-aspartate receptor modulation, dysregulation of lipid and glucose metabolism, and patient-specific risk factors for cognitive impairment. The purpose of this narrative review article is to describe the proposed mechanisms, incidence of neurocognitive adverse events from phase II and III trials for PCSK9 inhibitors, neurocognitive assessments utilized in clinical trials, and clinical implications. Given the increasing prevalence of PCSK9 inhibitor use and the neurocognitive adverse events observed with prior lipid-lowering therapies, clinicians should be aware of the risks associated with PCSK9 inhibitors, especially when therapy is indicated for patients at high risk for cardiovascular events. Overall, the incidence of PCSK9 inhibitor-associated neurocognitive appears to be uncommon. However, additional prospective studies evaluating cognitive impairment may be beneficial to determine the long-term safety of these agents.

scholarly journals Patient-Specific Predictive Antibiogram in Decision Support for Empiric Antibiotic Treatment

2020 ◽  
Vol 41 (S1) ◽  
pp. s521-s522
Author(s):  
Debarka Sengupta ◽  
Vaibhav Singh ◽  
Seema Singh ◽  
Dinesh Tewari ◽  
Mudit Kapoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Resistance ◽  
Model Building ◽  
Medical Center ◽  
Bacterial Species ◽  
Model Performance ◽  
The United States ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Comparative Performance

Background: The rising trend of antibiotic resistance imposes a heavy burden on healthcare both clinically and economically (US$55 billion), with 23,000 estimated annual deaths in the United States as well as increased length of stay and morbidity. Machine-learning–based methods have, of late, been used for leveraging patient’s clinical history and demographic information to predict antimicrobial resistance. We developed a machine-learning model ensemble that maximizes the accuracy of such a drug-sensitivity versus resistivity classification system compared to the existing best-practice methods. Methods: We first performed a comprehensive analysis of the association between infecting bacterial species and patient factors, including patient demographics, comorbidities, and certain healthcare-specific features. We leveraged the predictable nature of these complex associations to infer patient-specific antibiotic sensitivities. Various base-learners, including k-NN (k-nearest neighbors) and gradient boosting machine (GBM), were used to train an ensemble model for confident prediction of antimicrobial susceptibilities. Base learner selection and model performance evaluation was performed carefully using a variety of standard metrics, namely accuracy, precision, recall, F1 score, and Cohen &kappa;. Results: For validating the performance on MIMIC-III database harboring deidentified clinical data of 53,423 distinct patient admissions between 2001 and 2012, in the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center in Boston, Massachusetts. From ~11,000 positive cultures, we used 4 major specimen types namely urine, sputum, blood, and pus swab for evaluation of the model performance. Figure 1 shows the receiver operating characteristic (ROC) curves obtained for bloodstream infection cases upon model building and prediction on 70:30 split of the data. We received area under the curve (AUC) values of 0.88, 0.92, 0.92, and 0.94 for urine, sputum, blood, and pus swab samples, respectively. Figure 2 shows the comparative performance of our proposed method as well as some off-the-shelf classification algorithms. Conclusions: Highly accurate, patient-specific predictive antibiogram (PSPA) data can aid clinicians significantly in antibiotic recommendation in ICU, thereby accelerating patient recovery and curbing antimicrobial resistance.Funding: This study was supported by Circle of Life Healthcare Pvt. Ltd.Disclosures: None

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