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PURPOSE: Frailty is associated with adverse outcomes following radical cystectomy. Prospective tools to identify factors affecting outcomes are needed. We describe a novel electronic rapid fitness assessment to evaluate geriatric patients undergoing radical cystectomy. MATERIALS AND METHODS: Prior to undergoing radical cystectomy between February 2015 and February 2018, 80 patients over the age of 75 completed the electronic rapid fitness assessment and were perioperatively comanaged by the Geriatrics Service. Physical and cognitive function over 12 domains were evaluated and an accumulated geriatric deficit score compiled. Hospital length of stay, discharge disposition, unplanned intensive care unit admissions, urgent care visits, readmissions, complications, and deaths were assessed. RESULTS: Sixty-five patients who underwent radical cystectomy for bladder cancer without concomitant procedures completed the assessment. Median age was 80 (77, 84), and 52 (80%) were male. A higher proportion of patients with intensive care unit admission, urgent care visit, and major complications had impairments identified within electronic rapid fitness assessment domains, including Timed Up and Go. Readmission rates were similar between patients with or without deficits identified. Higher accumulated geriatric deficit score was significantly associated with intensive care unit admission (p=0.035), death within 90 days (p=0.037), and discharge to other than home (p=0.0002). CONCLUSIONS: We demonstrated the feasibility of assessing fitness in patients over 75 undergoing radical cystectomy using a novel electronic fitness tool. Physical limitations and overall impairment corresponded to higher intensive care unit admission rates and adverse postoperative outcomes. Larger studies in less resourced environments are required to validate these findings.

Advances in applied machine learning techniques for neuroimaging have encouraged scientists to implement models to diagnose brain disorders such as Alzheimer′s disease at early stages. Predicting the exact stage of Alzheimer′s disease is challenging; however, complex deep learning techniques can manage this with precision. While successful, these complex architectures are difficult to interrogate and computationally expensive. Therefore, using novel, simpler architectures with more efficient pattern extraction capabilities, such as transformers, is of interest to neuroscientists. This study introduced an optimized vision transformer architecture to predict the group membership by separating healthy adults, mild cognitive impairment, and Alzheimer′s′ brains within the same age group (>75 years) using resting-state functional (rs-fMRI) and structural magnetic resonance imaging (sMRI) data. Our optimized architecture known as OViTAD is currently the sole vision transformer-based end-to-end pipeline and outperformed the existing transformer models and most state-of-the-art solutions. Our model achieved F1-scores of 97%±0.0 and 99.55%±0.39 from the testing sets for the rs-fMRI and sMRI modalities in the triple-class prediction experiments. Furthermore, our model reached these performances using 30% fewer parameters than a vanilla transformer. The model was robust and repeatable, producing similar estimates across three runs (we reported the averaged evaluation metrics). Finally, to challenge the model, we observed how it handled increasing noise levels by inserting varying numbers of healthy brains into the two dementia groups. Our findings suggest that optimized vision transformers are a promising and exciting new approach for neuroimaging applications, especially for Alzheimer′s disease prediction.

Information technology (IT) companies implement multi-dimensional policy plans that include procedures, sub-plans, and instructions to outline their business scopes, targets, and communications. This work outlined the IT policy implementation plan designed by an imaginary company with a random name called Northcentral Cloud Consulting Firm (NCCF), containing proposed IT policies, milestones and roadmaps, control framework, stakeholder responsibilities, knowledge transfer plan, and leadership roles. As NCCF’s major customers seek data-driven solutions in cloud computing, the NCCF IT policy plan provides various data policies, including security and proper usage of machine learning services. The plan offers a detailed roadmap of its financial, geographical, and reputational expansion within three years. The IT policy plan also compromises an IT risk management, contingency, and emergency communication plan, mainly for protecting data and business continuity. Stakeholder responsibilities are incorporated into the IT policy plan, as NCCF considers any engagement with its customers as a collaborative effort in which both parties have and share several responsibilities.

Key Points Question Is collaboration between geriatricians and surgeons in the perioperative care of older patients with cancer associated with postoperative outcomes? Findings In this cohort study including 1892 patients aged 75 years and older, the adjusted probability of death within 90 days after surgery was 4.3% for patients who received geriatric comanagement of care, compared with 8.9% for patients who received care management from the surgical service only. Meaning These findings suggest that when feasible, older patients undergoing surgical treatment for cancer should receive geriatric care comanagement as part of their perioperative care., This cohort study examines outcomes associated with geriatric comanaged perioperative care vs care managed by the surgery team alone among patients aged 75 years and older with cancer., Importance Collaboration between geriatricians and surgeons in the perioperative treatment of older patients has been associated with improved outcomes in several nononcologic specialties. Similar associations may be possible among older patients with cancer. Objective To investigate the associations of geriatric comanagement of care for older patients undergoing cancer-related surgical treatment with 90-day postoperative mortality, rate of adverse surgical events, and postoperative use of inpatient supportive care services. Design, Setting, and Participants This retrospective cohort study assessed outcomes of patients who received geriatric comanaged care vs those who did not using multivariable logistic regression analysis, with 90-day mortality as the outcome and geriatric comanagement of care as the main variable, with adjustment for age, sex, American Society of Anesthesiology score, Memorial Sloan Kettering Frailty Index score, preoperative albumin level, operative time, and estimated blood loss. A similar model was used to assess the association of geriatric comanagement with adverse surgical events, defined as any major complication, readmission, or emergency department visit within 30 days. Patients aged 75 years and older who underwent an elective surgical procedure with a hospital stay of at least 1 day at a single tertiary-care cancer center between February 2015 and February 2018 were included. Data were analyzed from January to July 2019. Exposures Postoperative care comanaged by the geriatrics service and surgical service (geriatric comanagement group) vs by the surgical service only (surgical service group). Main Outcomes and Measures 90-day mortality, adverse surgical events, and use of supportive care services. Results Of 1892 patients included, 1020 (53.9%) received geriatric comanagement of care; these patients, compared with those who received care managed by the surgery service only, were older (mean [SD] age, 81 [4] years vs 80 [4] years; P

Mild cognitive impairment (MCI) represents the intermediate stage between normal cerebral aging and dementia associated with Alzheimer's disease (AD). Early diagnosis of MCI and AD through artificial intelligence has captured considerable scholarly interest; researchers hope to develop therapies capable of slowing or halting these processes. We developed a state-of-the-art deep learning algorithm based on an optimized convolutional neural network (CNN) topology called MCADNNet that simultaneously recognizes MCI, AD, and normally aging brains in adults over the age of 75 years, using structural and functional magnetic resonance imaging (fMRI) data. Following highly detailed preprocessing, four-dimensional (4D) fMRI and 3D MRI were decomposed to create 2D images using a lossless transformation, which enables maximum preservation of data details. The samples were shuffled and subject-level training and testing datasets were completely independent. The optimized MCADNNet was trained and extracted invariant and hierarchical features through convolutional layers followed by multi-classification in the last layer using a softmax layer. A decision-making algorithm was also designed to stabilize the outcome of the trained models. To measure the performance of classification, the accuracy rates for various pipelines were calculated before and after applying the decision-making algorithm. Accuracy rates of 99.77% ± 0.36% and 97.5% ± 1.16% were achieved for MRI and fMRI pipelines, respectively, after applying the decision-making algorithm. In conclusion, a cutting-edge and optimized topology called MCADNNet was designed and preceded a preprocessing pipeline; this was followed by a decision-making step that yielded the highest performance achieved for simultaneous classification of the three cohorts examined.

Background:The American Society of Anesthesiologists physical status (ASA PS) classification system is the most common method of assessing preoperative functional status. Comprehensive geriatric assessment (CGA) has been proposed as a supplementary tool for preoperative assessment of older adults. The goal of this study was to assess the correlation between ASA classification and CGA deficits among oncogeriatric patients and to determine the association of each with 6-month survival.Patients and Methods:Oncogeriatric patients (aged ≥75 years) who underwent preoperative CGA in an outpatient geriatric clinic at a single tertiary comprehensive cancer center were identified. All patients underwent surgery, with a hospital length of stay (LOS) ≥1 day and at least 6 months of follow-up. ASA classifications were obtained from preoperative anesthesiology notes. Preoperative CGA scores ranged from 0 to 13. Six-month survival was assessed using the Social Security Death Index.Results:In total, 81 of the 980 patients (8.3%) included in the study cohort died within 6 months of surgery. Most patients were classified as ASA PS III (85.4%). The mean number of CGA deficits for patients with PS II was 4.03, PS III was 5.15, and PS IV was 6.95 (PP=.01), 30-day ICU admissions (OR, 2.77;P=.003), hospital LOS (OR, 1.03;P=.02), and preoperative albumin level (OR, 0.36;P=.004). ASA classification was not associated with 6-month mortality.Conclusions:Number of CGA deficits was strongly associated with 6-month mortality; ASA classification was not. Preoperative CGA elicits critical information that can be used to enhance the prediction of postoperative outcomes among older patients with cancer.

BACKGROUND Surgery is a notable stressor for older adults with cancer, who often are medically and psychosocially complex. The current study examined rates of preoperative psychosocial risk factors in older adults with cancer who were undergoing elective surgery and the relationship between these risk factors and the provision of mental health services during the postoperative hospitalization. METHODS A total of 1211 patients aged ≥75 years who were referred to the geriatrics service at a comprehensive cancer center were enrolled. Patients underwent elective surgery with a length of stay of ≥3 days and were followed for at least 30 days after surgery. A comprehensive geriatric assessment was administered as part of routine preoperative care. Bivariate relationships between demographic and surgical characteristics and the preoperative comprehensive geriatric assessment and the receipt of mental health services during the postoperative hospitalization period were examined. Characteristics with bivariate relationships that were significant at the level of P

Objective To assess fitness and outcomes in older women undergoing cytoreductive surgery for advanced ovarian cancer (OC). Methods A prospective study of OC patients referred to Geriatrics Clinic for preoperative evaluation. All completed the electronic Rapid Fitness Assessment (eRFA) and were followed by Geriatrics Service during inpatient postoperative course, co-managed by Surgical Service. Outcomes were 30-day Intensive Care Unit (ICU) admission, emergency room (ER) visit, readmission, mortality, adverse surgical events. Descriptive statistics were used. Results Forty-two women (median age 79, range 74–88), 38 with newly diagnosed advanced OC, 4 with recurrent OC, underwent cytoreductive surgery between 5/2015 and 1/2018. Preoperative age-related impairments per eRFA: high level of distress (71%), functional dependency (59%), limited social activity (59%), depression (57%), slow Time Up and Go (54%), Karnofsky Performance Score (KPS) ≤ 80 (41%), poor social support (43%), polypharmacy (35%), weight loss>10 lbs. (25%), fall history (244%), cognitive impairment (13%). Median number of comorbid conditions = 3. Among 38 newly diagnosed women, 26 (68%) had stage IIIC, 11 (29%) stage IV. Sixteen (42%) underwent primary debulking surgery, 22 (58%) neoadjuvant chemotherapy followed by interval debulking surgery. Median duration of surgery = 245.5 min (range 95–621); median hospital length of stay = 6 days (range 0–22). Optimal debulking rate = 97%, complete gross resection rate = 63%. One patient was admitted to ICU, 26% had 30-day ER visit, 10% were readmitted. Any complication, minor complication, major complication occurred in 58%, 55%, 8%, respectively. Median time from surgery to postoperative chemotherapy = 34.5 days (range 19–66). Median follow-up = 15.7 months (range 3.7–38.0), 12-month survival = 93.3%. There was no 180-day mortality. Conclusion Cytoreductive surgery among older women with advanced OC and frailty can be performed safely in a tertiary care center with preoperative/postoperative geriatric and surgical co-management.

This study describes emergency department (ED) sepsis patients with non-critical serum venous lactate (LAC) levels (LAC4.0 mmol/L) who suffered in-hospital mortality and examines LAC in relation to survival times.An ED based retrospective cohort study accrued September 2010 to August 2014. Inclusion criteria were ED admission, LAC sampling,2 systemic inflammatory response syndrome criteria with an infectious source (sepsis), and in-hospital mortality. Kaplan-Meier curves were used for survival estimates. An a priori sub-group analysis for patients with repeat LAC within 6 hours of initial sampling was undertaken. The primary outcome was time to in-hospital death evaluated using rank-sum tests and regression models.One hundred ninety-seven patients met inclusion criteria. Pulmonary infections were the most common (44%) and median LAC was 1.9 mmol/L (1.5, 2.5). Thirteen patients (7%) died within 24 hours and 79% by ≤28 days. Median survival was 11 days (95% CI, 8.0-13). Sixty-two patients had repeat LAC sampling with 14 (23%) and 48 (77%) having decreasing increasing levels, respectively. No significant differences were observed in treatment requirements between the LAC subgroups. Among patients with decreasing LAC, median survival was 24 days (95% CI, 5-32). For patients with increasing LAC median survival was significantly shorter (7 days; 95% CI, 4-11, P = .04). Patients with increasing LAC had a non-significant trend toward reduced survival (HR = 1.6 95% CI, 0.90-3.0, P = .10).In septic ED patients experiencing in-hospital death, non-critical serum venous lactate may be utilized as a risk-stratifying tool for early mortality, while increasing LAC levels may identify those in danger of more rapid deterioration.

11512 Background: We explored the association between geriatric comanagement and 90-day postoperative mortality of cancer patients aged 75 or older. Methods: A retrospective review of a prospectively maintained database was performed on patients over 75 years old who underwent elective surgery with hospital length of stay of ≥1 day at Memorial Sloan Kettering Cancer Center from 2015-2018. Geriatric comanagement group (GCG) patients had geriatric preoperative evaluation and inpatient geriatric comanagement. Patients in the surgical management group (SMG) did not have geriatric preoperative evaluation or postoperative geriatric comanagement. We utilized a multivariable logistic regression model with 90-day mortality as the outcome, geriatric co-management as the predictor, and adjusted for age, gender, American Society of Anesthesiology score, Memorial Sloan Kettering Frailty Index, preoperative albumin level, operation time, and estimated blood loss. The same logistic model was used to assess the association between adverse surgical events within 30-days (any major complication, readmission, or urgent care center visit) and geriatric comanagement. Results: Of 1,855 patients (median age 80), 1,009 patients (54%) were co-managed by geriatricians. GCG patients were slightly older, less likely to be male, had longer operation time, and stayed in the hospital longer. Adjusted rates of 90-day mortality was lower in GCG vs. SMG (4.3% and 9.2%, respectively; 95% CI around difference -7.3%, -2.5%; p-value < 0.0001). We did not find evidence of a difference in adverse surgical events between groups (OR 0.96, p-value = 0.8). A greater proportion of GCG patients received inpatient physical therapy (80% vs. 64%) and occupational therapy (37% vs. 25%) compared to SMG patients. Conclusions: Our study shows that geriatric comanagement is associated with reduced 90-day postoperative mortality in cancer patients aged ≥75. A randomized trial study is needed to confirm this finding.

11542 Background: We have been prospectively collecting Geriatric Assessment (GA) data on patients aged 75+ undergoing oncologic surgery at a single institution. We aimed to to determine if a fitness scale based on a limited number of easily captured datafields can be developed, validated, and associated with 6 month postoperative mortality. Methods: From patients referred to Memorial Sloan Kettering (MSK) Geriatrics Service for preoperative GA since 2015, we have selected patients who were aged 75+, had elective surgery, with hospital length of stay (LOS) of ≥1 day, and had at least 6-month follow up. The Fitness Scale-4 item (FS4) is composed of 3 variables from the GA; Karnofsky Performance Scale (KPS), ability to walk outside, ability to perform housekeeping, and additional variable of preoperative albumin level. The FS4 total score ranges from 0-8 (frail = 0-2, vulnerable = 3-5, and fit = 6+). We validated this scale against prevalence of geriatric syndromes in each fitness category. We performed multivariable regression (MVR) analysis to assess the odds of six-month postoperative mortality among vulnerable and frail patients vs. fit patients. Results: Out of 1270 patients (median age 79, IQR 77-83), 792 (61.4%), 327 (25.7%) and 151 (11.9%) were fit, vulnerable, and frail, respectively. All of the geriatric syndromes were more prevalent among frail and vulnerable patients than fit patients. Six-month mortality rate was 4.7%, 12.5%, and 21.2% for fit, vulnerable, and frail patients, respectively. In the MVR analysis, and after adjustment for age, gender, ASA PS, LOS, operation time, KPS, and preoperative albumin, the odds of six-month mortality among frail (OR = 4.58, p = 0.004) and vulnerable patients (OR = 2.63, p = 0.004) vs. fit patients were much higher. Conclusions: Frail and vulnerable patients based on FS4 are at significantly higher risk for six-month postoperative mortality vs. fit patients. Future studies should assess the external validation of FS4, and its impact on surgical decision making, and perioperative care of older cancer patients.

Key Points Question Can a more clinically feasible version of the modified Frailty Index for older patients with cancer be developed? Findings In this cohort study of 1137 older patients with cancer, the Memorial Sloan Kettering–Frailty Index (MSK-FI) was associated with aging-related impairments. A higher score on the MSK-FI was also associated with a longer length of stay, higher odds of intensive care unit admission, and lower overall survival. Meaning The MSK-FI may be a feasible tool to perioperatively assess frailty in older patients with cancer., This cohort study evaluates the association of the Memorial Sloan Kettering–Frailty Index with established geriatric assessment and surgical outcomes for older surgical patients with cancer., Importance Frailty based on the modified Frailty Index is associated with poor postoperative outcomes. However, the index requires high levels of personnel time and effort and often has missing data. Objective To evaluate the association of the Memorial Sloan Kettering–Frailty Index (MSK-FI) with established geriatric assessment (GA) and surgical outcomes. Design, Setting, and Participants This cohort study included prospectively evaluated patients with cancer 75 years and older who were referred to MSK Geriatrics Service clinics for preoperative evaluation before undergoing surgery requiring hospitalization between February 2015 and September 2017. Patients were comanaged by the Geriatrics Service and Surgery Service in the postoperative period. Exposures Impairments identified by GA and comorbid conditions retrieved from submitted International Classification of Diseases, Ninth Revision (ICD-9) and ICD-10 codes within the first 48 hours of hospitalization. Main Outcomes and Measures The association of MSK-FI score (which included ICD-9 and ICD-10 codes) with GA impairments (based on clinical interview and examination as well as patient reports) was examined. The associations of MSK-FI score with short-term surgical outcomes (ie, frequency of complications, length of stay, 30-day surgical complications, 30-day intensive care unit admissions, and 30-day readmissions) and 1-year survival, estimated by Kaplan-Meier methods, were determined. Results In total, 1137 patients (median [interquartile range] age, 80 [77-84] years; 583 [51.2%] women) were included in the study. A higher MSK-FI score was associated with the number of GA impairments (ρ = 0.52; bootstrapped 95% CI, 0.47-0.56). Each 1-point increase in MSK-FI score was associated with longer length of stay (0.58 d; 95% CI, 0.22-0.95; P = .002) and higher odds of intensive care unit admission (odds ratio, 1.28; 95% CI, 1.04-1.58; P = .02). Median (interquartile range) follow-up among survivors was 12.1 (5.6-19.1) months. The MSK-FI score was associated with overall mortality; 12-month risk of death was 5% for a score of 0 and approximately 20% for scores of 4 and higher (nonlinear association, P = .005). Conclusions and Relevance In this study, the MSK-FI was associated with the previously validated GA and postoperative outcomes in older patients with cancer and may be a feasible tool for perioperative assessment of older surgical patients with cancer. Future studies should assess the association of MSK-FI score with postoperative care and outcomes of older, frail patients with cancer.

Over the past decade, machine learning techniques and in particular predictive modeling and pattern recognition in biomedical sciences, from drug delivery systems to medical imaging, have become one of the most important methods of assisting researchers in gaining a deeper understanding of issues in their entirety and solving complex medical problems. Deep learning is a powerful machine learning algorithm in classification that extracts low-to high-level features. In this paper, we employ a convolutional neural network to distinguish an Alzheimers brain from a normal, healthy brain. The importance of classifying this type of medical data lies in its potential to develop a predictive model or system in order to recognize the symptoms of Alzheimers disease when compared with normal subjects and to estimate the stages of the disease. Classification of clinical data for medical conditions such as Alzheimers disease has always been challenging, and the most problematic aspect has always been selecting the strongest discriminative features. Using the Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimers subjects from normal controls, where the accuracy of testing data reached 96.85%. This experiment suggests that the shift and scale invariant features extracted by CNN followed by deep learning classification represents the most powerful method of distinguishing clinical data from healthy data in fMRI. This approach also allows for expansion of the methodology to predict more complicated systems.

Recently, big data applications have been rapidly expanding into various industries. Healthcare is among those industries that are willing to use big data platforms, and as a result, some large data analytics tools have been adopted in this field. Medical imaging, which is a pillar in diagnostic healthcare, involves a high volume of data collection and processing. A massive number of 3D and 4D images are acquired in different forms and resolutions using a variety of medical imaging modalities. Preprocessing and analysis of imaging data is currently a costly and time-consuming process. However, few big data platforms have been created or modified for medical imaging purposes because of certain restrictions, such as data format. In this paper, we designed, developed and successfully tested a new pipeline for medical imaging data (in particular, functional magnetic resonance imaging — fMRI) using the Big Data Spark / PySpark platform on a single node, which allowed us to read and load imaging data, convert the data to Resilient Distributed Datasets in order to manipulate and perform in-memory data processing in parallel, and convert final results to an imaging format. Additionally, the pipeline provides an option to store the results in other formats, such as data frames. Using this new pipeline, we repeated our previous work, in which we extracted brain networks from fMRI data using template matching and the sum of squared differences (SSD) method. The final results revealed that our Spark (PySpark) based solution improved the performance (in terms of processing time) approximately fourfold when compared with the previous work developed in Python.

1AbstractTo extract patterns from neuroimaging data, various statistical methods and machine learning algorithms have been explored for the diagnosis of Alzheimer’s disease among older adults in both clinical and research applications; however, distinguishing between Alzheimer’s and healthy brain data has been challenging in older adults (age > 75) due to highly similar patterns of brain atrophy and image intensities. Recently, cutting-edge deep learning technologies have rapidly expanded into numerous fields, including medical image analysis. This paper outlines state-of-the-art deep learning-based pipelines employed to distinguish Alzheimer’s magnetic resonance imaging (MRI) and functional MRI (fMRI) from normal healthy control data for a given age group. Using these pipelines, which were executed on a GPU-based high-performance computing platform, the data were strictly and carefully preprocessed. Next, scale- and shift-invariant low- to high-level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture. In this study, fMRI data were used for the first time in deep learning applications for the purposes of medical image analysis and Alzheimer’s disease prediction. These proposed and implemented pipelines, which demonstrate a significant improvement in classification output over other studies, resulted in high and reproducible accuracy rates of 99.9% and 98.84% for the fMRI and MRI pipelines, respectively. Additionally, for clinical purposes, subject-level classification was performed, resulting in an average accuracy rate of 94.32% and 97.88% for the fMRI and MRI pipelines, respectively. Finally, a decision making algorithm designed for the subject-level classification improved the rate to 97.77% for fMRI and 100% for MRI pipelines.

Over the past decade, machine learning techniques and in particular predictive modeling and pattern recognition in biomedical sciences, from drug delivery systems to medical imaging, have become one of the most important methods of assisting researchers in gaining a deeper understanding of issues in their entirety and solving complex medical problems. Deep learning is a powerful machine learning algorithm in classification that extracts low- to high-level features. In this paper, we employ a convolutional neural network to distinguish an Alzheimer′s brain from a normal, healthy brain. The importance of classifying this type of medical data lies in its potential to develop a predictive model or system in order to recognize the symptoms of Alzheimer′s disease when compared with normal subjects and to estimate the stages of the disease. Classification of clinical data for medical conditions such as Alzheimer′s disease has always been challenging, and the most problematic aspect has always been selecting the strongest discriminative features. Using the Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimer′s subjects from normal controls, where the accuracy of testing data reached 96.85%. This experiment suggests that the shift and scale invariant features extracted by CNN followed by deep learning classification represents the most powerful method of distinguishing clinical data from healthy data in fMRI. This approach also allows for expansion of the methodology to predict more complicated systems.

172 Background: Older cancer patients are at higher risk for poor postoperative outcomes. We tested the validity and utility of Memorial Sloan Kettering-Frailty Index (MSK-FI) in this setting. Methods: In a single institution, prospective cohort study, patients age 75+ received comprehensive geriatric assessment (CGA) by the Geriatrics service during preoperative evaluation. The MSK-FI was developed based on the modified Frailty Index, incorporating 10 comorbid conditions and one item related to basic and instrumental activities of daily living. With the total score ranging from 0 to 11, a score of ≥ 3 was considered frail. We validated the MSK-FI against the CGA, and assessed the relationship between MSK-FI frailty with short term (hospital length of stay (LOS), intensive care unit (ICU) admission) and long-term (overall survival) postoperative outcomes utilizing multivariable linear, logistic, and cox regression models, adjusting for age, duration of surgery, American Society of Anesthesiologists physical status classification, and preoperative albumin level. Results: In total, 1,137 cancer patients (median age 80) were included in the study. The prevalence of frailty based on MSK-FI was 41%. Frail patients were more likely to have poor Karnofsky Performance Status (56% vs. 29%), be dependent in basic and instrumental activities of daily living (72% vs. 40% and 64% vs. 34%), experienced a fall in the past year (30% vs. 18%), have slower gait speed (49% vs. 22%),be depressed (66% vs. 49%), have limited social activity (62% vs. 43%), take ≥ 5 medications (63% vs. 25%), experienced significant weight loss (24% vs. 16%), and suffer from polycomorbid conditions (87% vs. 26%). Frailty was associated with longer LOS in the hospital (1.9 days, p

10035Background: Perioperative Geriatric Comanagement may improve surgical outcomes of older frail cancer patients. We present our 3 year experience with perioperative geriatric comanagement & the ...

10011Background: The relationship between age-related impairments in geriatric surgical patients and the need for postoperative intensive care unit (ICU) admission and subsequent outcomes is largel...

As the interest in functional connectivity continues to increase among neuroimaging researchers there becomes a greater need to develop an objective method of network identification. The current paper offers a solution to this problem by developing a robust decision making algorithm that can extract a target neural network from an array of spatial maps. We used a probabilistic independent component analysis to generate spatial maps of the Default Mode Network (DMN); however, this adaptive pipeline can be applied to any network of interest. Different template matching algorithms including: Normalized Cross-Correlation, Sum of Squared Differences and Dice Coefficient, were applied to the spatial and frequency domains of the dataset to identify the components that shared the greatest similarity to our DMN template. After identifying components within the resting state, the decision making pipeline selected the components within each method that had the highest matching scores to our DMN template. The final decision of selecting the most prototypical DMN components was made by a comparison between methods. This resulted in a DMN mask that was generated by the components chosen by our decision-making algorithm. To evaluate the accuracy of the decision-maker, a cross-correlation between each final mask and the template was measured. Results indicated that the Normalized Cross Correlation method, using both the spatial and frequency domain, and the Dice Coefficient method, generated the optimal DMN mask. This demonstrates the utility of our algorithm in providing an objective method for network extraction.

38 Background: Older cancer patients may need to make challenging treatment decisions at the time of preoperative evaluation. This study assesses the reasons and outcomes of older cancer patients who did not proceed with planned surgery. Methods: This is a retrospective analysis of a cohort of 1767 older cancer patients who presented to the Geriatrics clinic for preoperative evaluation between 1/2015 and 12/2016. Differences in Geriatric Assessment between those who proceeded and did not proceed with surgery were assessed within a month after preoperative evaluation. Charts of those who did not proceed with surgery were reviewed to identify the reasons, and outcomes of those patients. Results: In total, 114 out of 1767 (6.5%) did not proceed with surgery. Functional status played an important role in this decision. Among patients with Karnofsky Performance Scale (KPS) of 80+, 20% had their procedure in another institution; this was 0% for patients with KPS 70-. 61% of patients with KPS 50 opted for surveillance or supportive care, compared to 37% and 32% of those with KPS 60-70 and 80+, respectively. Reasons cited for not medically clearing the patients also differed. 60% and 69% of those with KPS 80+ and 60-70 were not cleared based on comorbid conditions, compared to 44% of those with KPS 50-. On the other hand, 44% of patients with KPS 50- had documented frailty as a reason for not getting clearance, compared to 15% and 23% of those with KPS 80+ and KPS 60-70. Conclusions: Geriatricians play an important role in helping patients reach proper medical decisions at the time of surgery. Future studies should look at various interventions that may assist older cancer patients/caregivers in their decision making at the time of surgery.

The human brain is a complicated network made-up of a large number of regions, which are structurally and/or functionally connected. Recently, neuroimaging studies using functional Magnetic Resonance Imaging have revealed that certain neural structures are highly active during periods of rest. Amongst several methods that have been developed to analyze resting-state fMRI data, Probabilistic Independent Component Analysis (PICA) is currently the most popular technique. The major challenge of using PICA is that resting-state networks are split into several components and visually extracting them can be difficult. In this paper, we propose a fast and precise algorithm based on advanced template matching in spatial domain such as Normalized Cross Correlation adapted to functional images in order to automatically extract the Default Mode Network (DMN) which is the task independent resting state network in the brain using PICA. We create a DMN template covering all reported regions in literature using two standard atlases. Ultimately, we reconstruct an image of the extracted DMN from PICA using an optimized decision making. Our approach was effective given that our algorithm results correlated highly with the DMN template.