Examination of the Screener and Opioid Assessment for Patients with Pain-Short Form (SOAPP-SF) in an oncology palliative medicine clinic.

2018 ◽  
Vol 36 (34_suppl) ◽  
pp. 196-196
Author(s):  
Rebecca Greiner ◽  
Danielle Boselli ◽  
Mariam Salib ◽  
Jai Narendra Patel
Keyword(s):  
At Risk ◽  
Short Form ◽  
Smoking Habit ◽  
Area Under The Curve ◽  
Symptom Assessment ◽  
Assessment System ◽  
Cutoff Score ◽  
Opioid Misuse ◽  
Logistic Regression Models ◽  
Patients At Risk

196 Background: The National Comprehensive Cancer Network states opioids can be used to treat cancer pain and prescribers should identify patients at risk for opioid misuse; research in this area is limited. In the non-cancer population, SOAPP-SF is a validated tool to predict aberrant drug behavior; a score of ≥ 4 (out of 20) is considered high risk. We performed a retrospective observational study to determine the utility of the SOAPP in identifying opioid misuse in the oncology population as measured by a non-compliant toxicology screen. Methods: Consecutive consults seen during a 6-month period completed the 5-question SOAPP-SF and Edmonton Symptom Assessment System (ESAS) form. Toxicology screens assessed non-compliance (i.e., absence of prescribed medications and/or presence of non-prescribed or illegal substances). Logistic regression models estimated the associations of composite and individual SOAPP-SF scores and ESAS symptom scores with non-compliant screens. Threshold analysis were conducted to identify an optimal SOAPP-SF cutoff. Results: Of 192 consults, 64 patients providing SOAPP-SF score and toxicology screen were evaluable. Mean age was 59 ± 9.8 years: 56% were female, 34% and 62% were African American and Caucasian respectively. Median SOAPP-SF score was 2 (range: [0, 12]). Non-compliant screens were observed in 31% of patients. The area under the curve (AUC) was 0.65. The validated SOAPP-SF cutoff score of ≥ 4 was associated with a sensitivity and specificity of 0.43 and 0.79, respectively (p = 0.082). Sensitivity (0.76) and specificity (0.72) were maximized at a cutoff score of ≥ 3 (p < 0.001). When evaluated individually, the SOAPP-SF question about smoking habit was associated with a non-compliant screen (p = 0.020). Increased ESAS pain scores were associated with SOAPP-SF score ≥ 3 (p = 0.013). Conclusions: SOAPP-SF can identify oncology patients at risk for opioid misuse. Preliminary analyses suggest a more appropriate threshold of identification is a score of ≥ 3 not ≥ 4. Future work will increase numbers of evaluable patients and examine other factors associated with opioid misuse.

Assessment of a new tool to improve case manager identification of delayed return to work in the first two weeks of a workers’ compensation claim

2020 ◽  
Vol 34 (5) ◽  
pp. 656-666 ◽  
Author(s):  
Ross A Iles ◽  
Luke R Sheehan ◽  
Cameron McR Gosling
Keyword(s):  
At Risk ◽  
Return To Work ◽  
Short Form ◽  
Area Under The Curve ◽  
Case Manager ◽  
Workers Compensation ◽  
Time Loss ◽  
Work Time ◽  
Logistic Regression Models ◽  
Modifiable Factors

Objective: To determine whether the Plan of Action for a Case (PACE) tool improved identification of workers at risk of delayed return to work. Design: Prospective cohort of workers with accepted workers’ compensation claims in the state of New South Wales, Australia. Interventions: The 41-item PACE tool was completed by the case manager within the first two weeks of a claim. The tool gathered information from the worker, employer and treating practitioner. Multivariate logistic regression models predicted work time loss of at least one and three months. Results: There were 524 claimants with complete PACE information. A total of 195 (37.2%) had work time loss of at least one month and 83 (15.8%) had time loss of at least three months. Being male, injury location, an Orebro Musculoskeletal Pain Screening Questionnaire–Short Form score >50, having a small employer, suitable duties not being available, being certified unfit, and the worker having low one-month recovery expectations predicted time loss of over one month. For three months, injury location, a Short Form Orebro score >50, no return-to-work coordinator, and being certified unfit were significant predictors. The model incorporating PACE information provided a significantly better prediction of both one- and three-month outcomes than baseline information (area-under-the-curve statistics—one month: 0.85 and 0.68, respectively; three months: 0.85 and 0.69, respectively; both P < 0.001) Conclusion: The PACE tool improved the ability to identify workers at risk of ongoing work disability and identified modifiable factors suited to case manager–led intervention.

Opioid Risk Screening in an Oncology Palliative Medicine Clinic

2020 ◽  
Vol 16 (11) ◽  
pp. e1332-e1342 ◽  
Author(s):  
Rebecca S. Greiner ◽  
Danielle Boselli ◽  
Jai N. Patel ◽  
Mariam Salib ◽  
Connie Edelen ◽  
...  
Keyword(s):  
High Risk ◽  
Short Form ◽  
Smoking Habit ◽  
Symptom Assessment ◽  
Depression Score ◽  
Primary Objective ◽  
Opioid Misuse ◽  
Edmonton Symptom Assessment Scale ◽  
Male Sex ◽  
Symptom Assessment Scale

PURPOSE: Little information exists on factors that predict opioid misuse in oncology. We adopted the Screener and Opioid Assessment for Patients With Pain–Short Form (SOAPP-SF) and toxicology testing to assess for opioid misuse risk. The primary objective was to (1) identify characteristics associated with a high-risk SOAPP-SF score and noncompliant toxicology test, and (2) determine SOAPP-SF utility to predict noncompliant toxicology tests. METHODS: From July 1, 2017, to December 31, 2017, new patients completed the Edmonton Symptom Assessment Scale (ESAS), SOAPP-SF, and narcotic use agreement. Toxicology test results were collected at subsequent visits. RESULTS: Of 223 distinct patients, 96% completed SOAPP-SF. Mean age was 61 ± 12.7 years, 58% were female, 68% were White, and 28% were Black. Eighty-three eligible patients (38%) completed toxicology testing. Younger age, male sex, and increased ESAS depression scores were associated with high-risk SOAPP-SF scores. Smoking habit was associated with an aberrant test. An SOAPP-SF score ≥ 3 predicted a noncompliant toxicology test. CONCLUSION: Male sex, young age, and higher ESAS depression score were associated with a high SOAPP-SF score. Smoking habit was associated with an aberrant test. An SOAPP-SF of ≥ 3 (sensitivity, 0.74; specificity, 0.64), not ≥ 4, was predictive of an aberrant test; however, performance characteristics were decreased from those published by Inflexxion, for ≥ 4 (sensitivity, 0.86; specificity, 0.67). The specificity warrants caution in falsely labeling patients. The SOAPP-SF may aid in meeting National Comprehensive Cancer Network recommendations to screen oncology patients for opioid misuse.

scholarly journals Gait Speed as a Predictor for Diabetes Incidence in People with or at Risk of Knee Osteoarthritis: A Longitudinal Analysis from the Osteoarthritis Initiative

2021 ◽  
Vol 18 (9) ◽  
pp. 4414
Author(s):  
Aqeel M. Alenazi ◽  
Bader A. Alqahtani ◽  
Vishal Vennu ◽  
Mohammed M. Alshehri ◽  
Ahmad D. Alanazi ◽  
...  
Keyword(s):  
At Risk ◽  
Gait Speed ◽  
Area Under The Curve ◽  
Incident Diabetes ◽  
Future Research ◽  
Cutoff Score ◽  
Diabetes Incidence ◽  
Knee Oa ◽  
Osteoarthritis Initiative

Background: This study examined the association between baseline gait speed with incident diabetes mellitus (DM) among people with or at elevated risk for knee OA. Materials and Methods: Participants from the Osteoarthritis Initiative, aged 45 to 79 years, where included. Participants with or at risk of knee OA from baseline to the 96-month visit were included. Participants with self-reported DM at baseline were excluded. DM incidence was followed over the 4-time points. Gait speed was measured at baseline using a 20-m walk test. Generalized estimating equations with logistic regression were utilized for analyses. Receiver operator characteristic curves and area under the curve were used to determine the cutoff score for baseline speed. Results: Of the 4313 participants included in the analyses (58.7% females), 301 participants had a cumulative incidence of DM of 7.0% during follow-up. Decreased gait speed was a significant predictor of incident DM (RR 0.44, p = 0.018). The threshold for baseline gait speed that predicted incident DM was 1.32 m/s with an area under the curve of 0.59 (p < 0.001). Conclusions: Baseline gait speed could be an important screening tool for identifying people at risk of incident diabetes, and the determined cutoff value for gait speed should be examined in future research.

scholarly journals Diagnostic Performance and Accuracy of the MNA-SF against GLIM Criteria in Community-Dwelling Older Adults from Poland

Nutrients ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 2183
Author(s):  
Aleksandra Kaluźniak-Szymanowska ◽  
Roma Krzymińska-Siemaszko ◽  
Marta Lewandowicz ◽  
Ewa Deskur-Śmielecka ◽  
Katarzyna Stachnik ◽  
...  
Keyword(s):  
At Risk ◽  
Diagnostic Performance ◽  
Nutritional Assessment ◽  
Short Form ◽  
Area Under The Curve ◽  
Mini Nutritional Assessment ◽  
Community Dwelling ◽  
Study Results ◽  
Community Dwelling Older Adults ◽  
Test Result

Up to 28% of elderly residents in Europe are at risk of malnutrition. As uniform diagnostic criteria for malnutrition have not been formulated, in autumn 2018, the Global Leadership Initiative on Malnutrition (GLIM) presented a consensus on its diagnosis. According to the consensus, the diagnosis of malnutrition requires a positive screening test result for the risk of malnutrition, and the presence of at least one etiologic and one phenotypic criterion. This study aimed to assess the diagnostic performance and accuracy of the Mini Nutritional Assessment—Short Form (MNA-SF) against GLIM criteria. The analysis involved 273 community-dwelling volunteers aged ≥ 60 years. All participants were screened for malnutrition with the MNA-SF questionnaire. Next, the GLIM phenotypic and etiologic criteria were assessed in all subjects. Based on the presence of at least one phenotypic and one etiologic criterion, malnutrition was diagnosed in more than one-third of participants (n = 103, 37.7%). According to the MNA-SF, only 7.3% of subjects had malnutrition, and 28.2% were at risk of malnutrition. The agreement between the MNA-SF score and the GLIM criteria were observed in only 22.3% of the population. The sensitivity and specificity of MNA-SF against the GLIM criteria were fair (59.2% and 78.8%, respectively). The area under the curve (AUC) was 0.77, indicating the fair ability of MNA-SF to diagnose malnutrition. Based on the present study results, the best solution may be an optional replacement of the screening tool in the first step of the GLIM algorithm with clinical suspicion of malnutrition.

Abstract 038: A Novel Histology Based Classification System to Identify Patients at Risk for Postoperative Atrial Fibrillation

2013 ◽  
Vol 113 (suppl_1) ◽  
Author(s):  
Mahek Mirza ◽  
Anton Strunets ◽  
Ekhson Holmuhamedov ◽  
Jasbir Sra ◽  
Paul H Werner ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
At Risk ◽  
Classification System ◽  
Area Under The Curve ◽  
Postoperative Atrial Fibrillation ◽  
Right Atrial ◽  
Class Iii ◽  
Atrial Fibrosis ◽  
Patients At Risk ◽  
The Impact

Postoperative atrial fibrillation (PoAF) is a common complication in up to 40% of patients after cardiac surgery, increasing morbidity, hospital stay and costs. The myocardial substrate underlying PoAF is not fully characterized. The objective was to assess the impact of atrial fibrosis on incident AF and define the fibrosis threshold level predictive of PoAF. Methods: Right atrial appendages removed from patients undergoing elective CABG with no history of AF or class III/IV heart failure were used to characterize the ratio of collagen to myocardium (Masson’s trichrome; NIH ImageJ software; Fig A), which was correlated with incident AF. Percentage burden of fibrosis predictive of PoAF with high sensitivity and specificity was determined by ROC curve. Results: Of 28 patients (67±10 years, 64% males), 15 had PoAF. There were no age, gender or comorbidity differences between groups. Compared to the group that remained in sinus rhythm, patients with PoAF had a significantly higher ratio of extracellular collagen to myocardium (45±16% vs. 5±4%, p <0.001; Fig B). A threshold ratio of 12.7% collagen to myocardium (ROC area under the curve 0.997; z statistic 137; P<0.0001) with 96% sensitivity and 97% specificity identified those with PoAF (Fig C). A classification system based on histological extent of atrial fibrosis is proposed for identifying patients at risk for PoAF (Fig D). Conclusion: Ongoing studies will confirm the predictive value of this new classification system for identifying the atrial substrate predisposing PoAF and correlate with preoperative cardiac imaging and circulatory serum biomarkers to provide a novel noninvasive tool to stratify patients at risk for PoAF.

scholarly journals MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model

2020 ◽  
Vol 9 (2) ◽  
pp. 343 ◽  
Author(s):  
Arash Kia ◽  
Prem Timsina ◽  
Himanshu N. Joshi ◽  
Eyal Klang ◽  
Rohit R. Gupta ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Area Under The Curve ◽  
Learning Model ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Support Vector ◽  
Adult Age ◽  
Machine Learning Model ◽  
Patients At Risk

Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.

scholarly journals Predicting Persistent Opioid Use, Abuse, and Toxicity Among Cancer Survivors

2019 ◽  
Vol 112 (7) ◽  
pp. 720-727 ◽  
Author(s):  
Lucas K Vitzthum ◽  
Paul Riviere ◽  
Paige Sheridan ◽  
Vinit Nalawade ◽  
Rishi Deka ◽  
...  
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
Cancer Survivors ◽  
Critical Role ◽  
Multivariable Analysis ◽  
Area Under The Curve ◽  
Opioid Abuse ◽  
Opioid Use ◽  
Multivariable Logistic Regression Model ◽  
Patients At Risk

Abstract Background Although opioids play a critical role in the management of cancer pain, the ongoing opioid epidemic has raised concerns regarding their persistent use and abuse. We lack data-driven tools in oncology to understand the risk of adverse opioid-related outcomes. This project seeks to identify clinical risk factors and create a risk score to help identify patients at risk of persistent opioid use and abuse. Methods Within a cohort of 106 732 military veteran cancer survivors diagnosed between 2000 and 2015, we determined rates of persistent posttreatment opioid use, diagnoses of opioid abuse or dependence, and admissions for opioid toxicity. A multivariable logistic regression model was used to identify patient, cancer, and treatment risk factors associated with adverse opioid-related outcomes. Predictive risk models were developed and validated using a least absolute shrinkage and selection operator regression technique. Results The rate of persistent opioid use in cancer survivors was 8.3% (95% CI = 8.1% to 8.4%); the rate of opioid abuse or dependence was 2.9% (95% CI = 2.8% to 3.0%); and the rate of opioid-related admissions was 2.1% (95% CI = 2.0% to 2.2%). On multivariable analysis, several patient, demographic, and cancer and treatment factors were associated with risk of persistent opioid use. Predictive models showed a high level of discrimination when identifying individuals at risk of adverse opioid-related outcomes including persistent opioid use (area under the curve [AUC] = 0.85), future diagnoses of opioid abuse or dependence (AUC = 0.87), and admission for opioid abuse or toxicity (AUC = 0.78). Conclusion This study demonstrates the potential to predict adverse opioid-related outcomes among cancer survivors. With further validation, personalized risk-stratification approaches could guide management when prescribing opioids in cancer patients.

Avoiding Opioid Misuse After Surgery in the Era of the Opioid Epidemic

2020 ◽  
Vol 86 (11) ◽  
pp. 1565-1572
Author(s):  
Michael J. Petersen ◽  
Kathryne W. Adams ◽  
Nicole F. Siparsky
Keyword(s):  
At Risk ◽  
Monitoring Program ◽  
Opioid Therapy ◽  
Surgical Patients ◽  
Educational Video ◽  
Aberrant Behavior ◽  
Opioid Misuse ◽  
Video Viewing ◽  
Patients At Risk ◽  
The Impact

Background Chronic pain patients at risk of addiction can be identified through pre-prescription screening with the opioid risk tool (ORT); there is no equivalent for surgical patients. Our aims were to validate the ORT in the surgical population and assess the impact of patient education on compliance with proper storage and disposal (S&D) of unused opioid therapy (UOT). Methods Each subject completed the ORT, prevideo and postvideo surveys, educational video viewing, and compliance survey. Aberrant behavior was assessed by questionnaire, chart review, and Illinois Prescription Monitoring Program review. Results We recruited 24 subjects who underwent emergency surgery; 18 (of 24) were prescribed an opioid on discharge and 15 (of 18) were followed for 1 month. Before education, 38% (n = 9 of 24) of subjects identified proper UOT disposal and 63% (n = 15 of 24) identified safe handling of opioids. After education, 75% (n = 18 of 24) identified proper S&D. On ORT, 9 of 24 subjects (38%) scored moderate-risk to high-risk for opioid misuse. Half of subjects who demonstrated aberrant behavior (n = 7 of 12, 58%) scored in the low-risk range on ORT; 67% of subjects (n = 10 of 15) retained UOT, and 67% (n = 10 of 15) safely stored UOT. Few subjects (30%; n = 3 of 10) who stored their UOT reported proper disposal of UOT. Discussion The ORT is not useful in identifying acute pain surgical patients at risk for aberrant behavior. An educational video increased awareness of, but not compliance with, safe S&D of UOT. Opioid overprescription continues to contribute to opioid misuse.

scholarly journals Computed-Tomography Body Composition Analysis Complements Pre-Operative Nutrition Screening in Colorectal Cancer Patients on an Enhanced Recovery after Surgery Pathway

Nutrients ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3745
Author(s):  
Pamela Klassen ◽  
Vickie Baracos ◽  
Leah Gramlich ◽  
Gregg Nelson ◽  
Vera Mazurak ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Computed Tomography ◽  
At Risk ◽  
Cancer Patients ◽  
Short Form ◽  
Composition Analysis ◽  
Nutrition Screening ◽  
Patients At Risk ◽  
Poor Outcomes

Pre-operative nutrition screening is recommended to identify cancer patients at risk of malnutrition, which is associated with poor outcomes. Low muscle mass (sarcopenia) and lipid infiltration to muscle cells (myosteatosis) are similarly associated with poor outcomes but are not routinely screened for. We investigated the prevalence of sarcopenia and myosteatosis across the nutrition screening triage categories of the Patient-Generated Subjective Global Assessment Short Form (PG-SGASF) in a pre-operative colorectal cancer (CRC) cohort. Data were prospectively collected from patients scheduled for surgery at two sites in Edmonton, Canada. PG-SGASF scores ≥ 4 identified patients at risk for malnutrition; sarcopenia and myosteatosis were identified using computed-tomography (CT) analysis. Patients (n = 176) with a mean age of 63.8 ± 12.0 years, 52.3% male, 90.3% with stage I–III disease were included. Overall, 25.2% had PG-SGASF score ≥ 4. Sarcopenia alone, myosteatosis alone or both were identified in 14.0%, 27.3%, and 6.4% of patients, respectively. Sarcopenia and/or myosteatosis were identified in 43.4% of those with PG-SGASF score < 4 and in 58.5% of those with score ≥ 4. Overall, 32.9% of the cohort had sarcopenia and/or myosteatosis with PG-SGASF score < 4. CT-defined sarcopenia and myosteatosis are prevalent in pre-operative CRC patients, regardless of the presence of traditional nutrition risk factors (weight loss, problems eating); therefore, CT image analysis effectively adds value to nutrition screening by identifying patients with other risk factors for poor outcomes.

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