The impact of frailty on mortality in non-surgical head and neck cancer treatment: Shifting the clinical paradigm.

OBJECTIVE: Compare survival of head and neck cancer (HNC) patients treated with surgical or non-surgical management according to frailty, quantify frailty with the Risk Analysis Index (RAI), a validated 14-item instrument. MATERIALS AND METHODS: Prospective cohort study of newly diagnosed HNC patients (≥18 years) who had frailty assessment from April 13, 2016 to September 30, 2016. Primary outcome was overall survival at 1- and 3-years. Cox proportional hazard models were utilized to examine mortality with predictor variables. Adjusted and unadjusted (Kaplan-Meier) survival curves stratified by either RAI scores or treatment modality were plotted. Kruskal-Wallis and likelihood ratio chi-square tests were used for comparing clinicodemographic variables. RESULTS: Of 165 patients, 54 (32.7%) were managed non-surgically, 49 (29.7%) were treated with definitive surgery only, and 62 (37.6%) were treated with multimodality (surgery + adjuvant) therapy. Among the full cohort and subgroup analysis of the frail/very frail (RAI ≥ 37), non-surgical patients had worse or similar 3-year survival than those treated with surgery +/- adjuvant therapy. Multivariable Cox proportional hazard models demonstrate that frail patients treated non-surgically experienced worse survival than their counterparts treated with surgery (HR = 2.50, p = 0.015, 95% CI: 1.19, 5.23) or multimodality therapy (HR = 3.91, p < 0.001, 95% CI: 1.94-7.89). CONCLUSION: Across all levels of frailty, long term survival of HNC patients treated without surgery is either worse than or like those treated with surgery. These findings (1) challenge current practices of steering patients "too frail for surgery" towards non-surgical, "non-invasive" therapy, and (2) suggest equipoise warranting randomized trials to clarify treatment of frail patients.

SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care.

BACKGROUND: Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer. METHODS AND FINDINGS: All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance. The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891-0.916]), c = 0.877 [95% CI:0.864-0.890]), and c = 0.869 [95% CI:0.857-0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4-5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751-0.921], 0.815 [95% CI: 0.730-0.900], and 0.794 [95% CI: 0.725-0.864], respectively). CONCLUSIONS: The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.

Small pancreatic neuroendocrine tumors: Resect or enucleate?

BACKGROUND: The aim of this analysis is to compare the postoperative outcomes of resection and enucleation of small pancreatic neuroendocrine tumors (PNETs). METHODS: The 2014-17 American College of Surgeons-NSQIP dataset was queried. Patients undergoing pancreatoduodenectomy (N = 297) or distal pancreatectomy (N = 712) for nonfunctional, small PNETs (T1/T2) were compared to 127 patients (11%) who were enucleated. RESULTS: Operative time (170 vs 261, p < 0.01) and transfusions were less in the enucleation cohort (1.6% vs 6.7% p < 0.01). There was no difference in postoperative pancreatic fistulas, but morbidity was lower in enucleated patients (36.2% vs 48.7% p < 0.01). Fifteen resected patients died postoperatively (1.5%) while all enucleated patients survived (p = 0.058). Mean postoperative length of stay was shorter after enucleation (5.7 vs 7.2 days p < 0.01). CONCLUSIONS: Enucleation of PNETs is performed in only 11% of patients, but takes less time, requires fewer transfusions, and is associated with reduced morbidity and shorter length of stay than resection.

Optimal Pancreatic Surgery: Are We Making Progress in North America?

OBJECTIVE: Our aims were to assess North American trends in the management of patients undergoing pancreatoduodenectomy (PD) and distal pancreatectomy (DP), and to quantify the delivery of optimal pancreatic surgery. BACKGROUND: Morbidity after pancreatectomy remains unacceptably high. Recent literature suggests that composite measures may more accurately define surgical quality. METHODS: The 2013 to 2017 American College of Surgeons National Surgical Quality Improvement Program Participant Use Files were queried to identify patients undergoing PD (N = 16,222) and DP (N = 7946). Patient, process, procedure, and 30-day postoperative outcome variables were analyzed over time. Optimal pancreatic surgery was defined as the absence of postoperative mortality, serious morbidity, percutaneous drainage, and reoperation while achieving a length of stay equal to or less than the 75th percentile (12 days for PD and 7 days for DP) with no readmissions. Risk-adjusted time-trend analyses were performed using logistic regression, and the threshold for statistical significance was P ≤ 0.05. RESULTS: The use of minimally invasive PD did not change over time, but robotic PD increased (2.5 to 4.2%; P < 0.001) and laparoscopic PD decreased (5.8% to 4.3%; P < 0.02). Operative times decreased (P < 0.05) and fewer transfusions were administered (P < 0.001). The percentage of patients with a drain fluid amylase checked on postoperative day 1 increased (P < 0.001), and a greater percentage of surgical drains were removed by postoperative day 3 (P < 0.001). Overall morbidity (P < 0.02), mortality (P < 0.05), and postoperative length of stay (P = 0.002) decreased. Finally, the rate of optimal pancreatic surgery increased for PD (53.7% to 56.9%; P < 0.01) and DP (53.3% to 58.5%; P < 0.001), and alspo for patients with pancreatic cancer (P < 0.01). CONCLUSIONS: From 2013 to 2017, pre, intra, and perioperative pancreatectomy processes have evolved, and multiple postoperative outcomes have improved. Thus, in 4 years, optimal pancreatic surgery in North America has increased by 3% to 5%.

Clinical Utility of the Risk Analysis Index as a Prospective Frailty Screening Tool within a Multi-practice, Multi-hospital Integrated Healthcare System.

OBJECTIVE: The goal of this project was to first address barriers to implementation of the Risk Analysis Index (RAI) within a large, multi-hospital, integrated healthcare delivery system, and to subsequently demonstrate its utility for identifying at-risk surgical patients. BACKGROUND: Prior studies demonstrate the validity of the RAI for evaluating preoperative frailty, but they have not demonstrated the feasibility of its implementation within routine clinical practice. METHODS: Implementation of the RAI as a frailty screening instrument began as a quality improvement initiative at the University of Pittsburgh Medical Center in July 2016. RAI scores were collected within a REDCap survey instrument integrated into the outpatient electronic health record and then linked to information from additional clinical datasets. NSQIP-eligible procedures were queried within 90 days following the RAI, and the association between RAI and postoperative mortality was evaluated using logistic regression and Cox proportional hazards models. Secondary outcomes such as inpatient length of stay and readmissions were also assessed. RESULTS: RAI assessments were completed on 36,261 unique patients presenting to surgical clinics across five hospitals from July 1 to December 31, 2016, and 8,172 of these underwent NSQIP-eligible surgical procedures. The mean RAI score was 23.6 (SD 11.2), the overall 30-day and 180-day mortality after surgery was 0.7% and 2.6%, respectively, and the median time required to collect the RAI was 33 [IQR 23-53] seconds. Overall clinic compliance with the recommendation for RAI assessment increased from 58% in the first month of the study period to 84% in the sixth and final month. RAI score was significantly associated with risk of death (HR=1.099 [95% C.I.: 1.091 - 1.106], p < 0.001). At an RAI cutoff of ≥37, the positive predictive values for 30- and 90-day readmission were 14.8% and 26.2%, respectively, and negative predictive values were 91.6% and 86.4%, respectively. CONCLUSIONS: The RAI frailty screening tool can be efficiently implemented within multi-specialty, multi-hospital healthcare systems. In the context of our findings and given the value of the RAI in predicting adverse postoperative outcomes, health systems should consider implementing frailty screening within surgical clinics.

Development of a Novel Pancreatoduodenectomy-Specific Risk Calculator: an Analysis of 10,000 Patients.

BACKGROUND: Pancreatoduodenectomy (PD) is often performed in frail patients and is associated with significant morbidity. The five-factor modified frailty index (mFI-5) has been utilized to predict adverse postoperative outcomes, but has not been tested in PD. We aimed to develop risk tools to generate and predict 30-day outcomes after PD and compare their performance with the mFI-5. Risk tools were then used to generate a PD-specific calculator. METHODS: Elective PDs from the 2014-2016 ACS NSQIP® Procedure Targeted Pancreatectomy PUFs were identified. Multivariable logistic regression models were constructed to predict postoperative mortality, any complication, serious complication, clinically relevant postoperative pancreatic fistula (CR-POPF), and discharge not-to-home. Predictive accuracy was evaluated through repeated stratified tenfold cross-validation and compared to the mFI-5. RESULTS: Nine thousand eight hundred sixty-seven PDs were captured. Nine risk factors were retained: sex, age, BMI, DM, HTN, ASA classification, pancreatic duct size, gland texture, and adenocarcinoma. Cross-validated C-indices ranged from 0.49 to 0.61 for the mFI-5 and 0.63 to 0.75 for our risk models. The best-performing model was for discharge not-to-home (C = 0.75), and the model delivering the largest increase in predictive accuracy was for CR-POPF (CmFI-5/Model = 0.49/0.70). A user-friendly risk calculator was created predicting the five outcomes of interest. CONCLUSION: We have created a PD-specific risk calculator that outperforms the mFI-5. This calculator may serve as a useful adjunct in shared decision-making for patients and surgeons.

Novel bypass risk predictive tool is superior to the 5-Factor Modified Frailty Index in predicting postoperative outcomes.

OBJECTIVE: This study aimed to develop risk predictive models of 30-day mortality, morbidity, and major adverse limb events (MALE) after bypass surgery for aortoiliac occlusive disease (AIOD) and to compare their performances with a 5-Factor Frailty Index. METHODS: The American College of Surgeons National Surgical Quality Improvement Program 2012-2017 Procedure Targeted Aortoiliac (Open) Participant Use Data Files were queried to identify all patients who had elective bypass for AIOD: femorofemoral bypass, aortofemoral bypass, and axillofemoral bypass (AXB). Outcomes assessed included mortality, major morbidity, and MALE within 30 days postoperatively. Major morbidity was defined as pneumonia, unplanned intubation, ventilator support for >48 hours, progressive or acute renal failure, cerebrovascular accident, cardiac arrest, or myocardial infarction. Demographics, comorbidities, procedure type, and laboratory values were considered for inclusion in the risk predictive models. Logistic regression models for mortality, major morbidity and MALE were developed. The discriminative ability of these models (C-indices) were compared with that of the 5-Factor Modified Frailty Index (mFI-5): a general frailty tool determined from diabetes, functional status, history of chronic obstructive pulmonary disease, history of congestive heart failure, and hypertension. Calculators were derived using the most significant variables for each of the three risk predictive models. RESULTS: A total of 2612 cases (mean age 65.0, 60% male) were identified, of which 1149 (44.0%) were femorofemoral bypass, 1138 (43.6%) were aortofemoral bypass, and 325 (12.4%) were axillofemoral bypass. Overall, the rates of mortality, major morbidity, and MALE were 2.0%, 8.5%, and 4.9%, respectively. Twenty preoperative risk factors were considered for incorporation in the risk tools. Apart from procedure type, age was the most statistically significant predictor of both mortality and morbidity. Preoperative anemia and critical limb ischemia were the most significant predictors of MALE. All three constructed models demonstrated significantly better discriminative ability (P < .001) on the outcomes of interest as compared with the mFI-5. CONCLUSIONS: Our models outperformed the mFI-5 in predicting 30-day mortality, major morbidity, and adverse limb events in patients with AIOD undergoing elective bypass surgery. Calculators were created using the most statistically significant variables to help calculate individual patient's postoperative risks and allow for better informed consent and risk-adjusted comparison of provider outcomes.

Validation of the Risk Analysis Index for Evaluating Frailty in Ambulatory Patients.

BACKGROUND: Frailty is a marker of dependency, disability, hospitalization, and mortality in community-dwelling older adults. However, existing tools for measuring frailty are too cumbersome for rapid point-of-care assessment. The Risk Analysis Index (RAI) of frailty is validated in surgical populations, but its performance outside surgical populations is unknown. OBJECTIVE: Validate the RAI in ambulatory patients. DESIGN, SETTING, AND PARTICIPANTS: Observational cohort study of outpatient surgical clinics within the University of Pittsburgh Medical Center Healthcare System between July 1, 2016, and December 31, 2016. Frailty was assessed using the RAI. Current Procedural Terminology codes following RAI assessment identified patients with and without minor office-based procedures (eg, joint injection, laryngoscopy). MAIN OUTCOMES AND MEASURES: All-cause 1-year mortality, assessed by stratified Cox proportional hazard models. RESULTS: Of 28,059 patients, 13,861 were matched to a minor, office-based procedure and 14,198 did not undergo any procedure. The mean (SD) age was 56.7 (17.2) years; women constituted 15,797 (56.3%) of the cohort. Median time (interquartile range 25th-75th percentile) to measure RAI was 30 (22-47) seconds. Mortality among the frail was two to five times that of patients with normal RAI scores. For example, the hazard ratio for frail ambulatory patients without a minor procedure was 3.69 (95% confidence interval [CI] = 2.51-5.41), corresponding to 30-, 180-, and 365-day mortality rates of 2.9%, 11.2%, and 17.4%, respectively, compared to 0.3%, 2.3%, and 4.0% among patients with normal RAI scores. Discrimination of mortality (overall, and censored at 30, 180, and 365 days) was excellent, ranging from c = 0.838 (95% CI = 0.773-0.902) for 30-day mortality after minor procedures to c = 0.909 (95% CI = 0.855-0.964) without a procedure. CONCLUSION: RAI is a valid, easily administered tool for point-of-care frailty assessment in ambulatory populations that may help clinicians and patients make better informed decisions about care choices-especially among patients considered high risk with a potentially limited life span. J Am Geriatr Soc 68:1818-1824, 2020.

Recalibration and External Validation of the Risk Analysis Index: A Surgical Frailty Assessment Tool.

OBJECTIVE AND BACKGROUND: The Risk Analysis Index (RAI) predicts 30-, 180-, and 365-day mortality based on variables constitutive of frailty. Initially validated, in a single-center Veteran hospital, we sought to improve model performance by recalibrating the RAI in a large, veteran surgical registry, and to externally validate it in both a national surgical registry and a cohort of surgical patients for whom RAI was measured prospectively before surgery. METHODS: The RAI was recalibrated among development and confirmation samples within the Veterans Affairs Surgical Quality Improvement Program (VASQIP; 2010-2014; N = 480,731) including major, elective noncardiac surgery patients to create the revised RAI (RAI-rev), comparing discrimination and calibration. The model was tested externally in the American College of Surgeons National Surgical Quality Improvement Program dataset (NSQIP; 2005-2014; N = 1,391,785), and in a prospectively collected cohort from the Nebraska Western Iowa Health Care System VA (NWIHCS; N = 6,856). RESULTS: Recalibrating the RAI significantly improved discrimination for 30-day [c = 0.84-0.86], 180-day [c = 0.81-0.84], and 365-day mortality [c = 0.78-0.82] (P < 0.001 for all) in VASQIP. The RAI-rev also had markedly better calibration (median absolute difference between observed and predicted 180-day mortality: decreased from 8.45% to 1.23%). RAI-rev was highly predictive of 30-day mortality (c = 0.87) in external validation with excellent calibration (median absolute difference between observed and predicted 30-day mortality: 0.6%). The discrimination was highly robust in men (c = 0.85) and women (c = 0.89). Discrimination also improved in the prospectively measured cohort from NWIHCS for 180-day mortality [c = 0.77 to 0.80] (P < 0.001). CONCLUSIONS: The RAI-rev has improved discrimination and calibration as a frailty-screening tool in surgical patients. It has robust external validity in men and women across a wide range of surgical settings and available for immediate implementation for risk assessment and counseling in preoperative patients.

Does Preoperative MELD Score Predict Adverse Outcomes Following Pancreatic Resection: an ACS NSQIP Analysis.

BACKGROUND: Higher MELD scores correlate with adverse operative outcomes regardless of the presence of liver disease, but their impact on pancreatectomy outcomes remains undefined. We aimed to compare 30-day adverse postoperative outcomes of patients undergoing elective pancreatectomy stratified by MELD score. METHODS: Elective pancreatoduodenectomies (PDs) and distal pancreatectomies (DPs) were identified from the 2014-2016 ACS NSQIP Procedure Targeted Pancreatectomy Participant Use Data Files. Outcomes examined included mortality, cardiopulmonary complications, prolonged postoperative length-of-stay, discharge not-to-home, transfusion, POPF, CR-POPF, any complication, and serious complication. Outcomes were compared between MELD score strata (< 11 vs. ≥ 11) as established by the United Network for Organ Sharing (UNOS). Multivariable logistic regression models were constructed to examine the risk-adjusted impact of MELD score on outcomes. RESULTS: A total of 7580 PDs and 3295 DPs had evaluable MELD scores. Of these, 1701 PDs and 223 DPs had a MELD score ≥ 11. PDs with MELD ≥ 11 exhibited higher risk for mortality (OR = 2.07, p < 0.001), discharge not-to-home (OR = 1.26, p = 0.005), and transfusion (OR = 1.7, p < 0.001). DP patients with MELD ≥ 11 demonstrated prolonged LOS (OR = 1.75, p < 0.001), discharge not-to-home (OR = 1.83, p = 0.01), and transfusion (OR = 2.78, p < 0.001). In PD, MELD ≥ 11 was independently predictive of 30-day mortality (OR = 1.69, p = 0.007) and transfusion (OR = 1.55, p < 0.001). In DP, MELD ≥ 11 was independently predictive of prolonged LOS (OR = 1.42, p = 0.026) and transfusion (OR = 2.3, p < 0.001). CONCLUSION: A MELD score ≥ 11 is associated with a near twofold increase in the odds of mortality following pancreatoduodenectomy. The MELD score is an objective assessment that aids in risk-stratifying patients undergoing pancreatectomy.

Dynamic simulation of crime perpetration and reporting to examine community intervention strategies.

OBJECTIVE: To develop a conceptual computational agent-based model (ABM) to explore community-wide versus spatially focused crime reporting interventions to reduce community crime perpetrated by youth. METHOD: Agents within the model represent individual residents and interact on a two-dimensional grid representing an abstract nonempirically grounded community setting. Juvenile agents are assigned initial random probabilities of perpetrating a crime and adults are assigned random probabilities of witnessing and reporting crimes. The agents' behavioral probabilities modify depending on the individual's experience with criminal behavior and punishment, and exposure to community crime interventions. Cost-effectiveness analyses assessed the impact of activating different percentages of adults to increase reporting and reduce community crime activity. Community-wide interventions were compared with spatially focused interventions, in which activated adults were focused in areas of highest crime prevalence. RESULTS: The ABM suggests that both community-wide and spatially focused interventions can be effective in reducing overall offenses, but their relative effectiveness may depend on the intensity and cost of the interventions. Although spatially focused intervention yielded localized reductions in crimes, such interventions were shown to move crime to nearby communities. Community-wide interventions can achieve larger reductions in overall community crime offenses than spatially focused interventions, as long as sufficient resources are available. CONCLUSION: The ABM demonstrates that community-wide and spatially focused crime strategies produce unique intervention dynamics influencing juvenile crime behaviors through the decisions and actions of community adults. It shows how such models might be used to investigate community-supported crime intervention programs by integrating community input and expertise and provides a simulated setting for assessing dimensions of cost comparison and intervention effect sustainability. ABM illustrates how intervention models might be used to investigate community-supported crime intervention programs.