Risk Calculator Research Articles

Morbidity related to major lung thoracoscopic resections in children.

In pediatric thoracic surgery, reported predictors for increased risk are symptoms and active/previous infections (RAP). We investigated the adverse events related to Video-Assisted Thoracic Surgery (VATS) in pediatric patients when considering RAP predictors. A retrospective analysis of pediatric VATS major lung resections in 2008-2021 was conducted at three institutions. We employed the pediatric surgical risk calculator to define patients' preoperative predicted risk (PredR). Postoperative complications were classified according to the Thoracic Morbidity & Mortality (TM&M) system. The observed TM&M rate (ObsR) and the PredR were compared. A subgroup analysis by RAP predictors was conducted. 37 patients (54% female) were included. Mean age and weight were 5.8 years and 22.8 kg. 56.7% had respiratory symptoms, 38.9% active infection and 59.5% history of infections (RAP subpopulations). VATS procedures were lobectomy (n=32), segmentectomy (n=3), bilobectomy (n=1) and pneumonectomy (n=1). The conversion rate was 5.4%. The mean PredR was of 4.43% (±1.8) and the overall ObsR was 45.94% with a median severity of II (I-III). This difference was significant and a higher PredR was not associated with complications development. PredR does not show association among the RAP vs non-RAP group. ObsR showed positive association with RAP, even if it reached statistical significance only for "respiratory symptoms" risk factor. ObsR reflected the number of bronchiectasis patients in our series (n=9), aligning with the hypothesis of "earlier and safer surgery". The risk calculator underestimates VATS morbidity. Multicentre studies will clarify the correlation between inflammation and surgical adverse events.

Prevalence of Cardiovascular Disease and Comparison of Risk Category Predictions of Systemic Coronary Risk Evaluation Score-2 and 4 Other Cardiovascular Disease Risk Assessment Tools Among People Living with Human Immunodefficiency Virus in Türkiye.

Cardiovascular disease (CVD) is a major cause of mortality among people living with HIV (PLWH). We aimed to assess the prevalence of diagnosed CVD and the risk of CVD among PLWH using 5 different tools. This retrospective, cross-sectional study was conducted in 20 tertiary centers in Türkiye between October 2021 and March 2022, among 1425 PLWH aged 40-75 years. About 82.7% were male, with a median age of 51. Web-based tools for each score were used for CVD risk calculations. Of 1425 PLWH enrolled, 10.8% had confirmed CVD, and 1132 had their risk scores evaluated. Of those participants, 42.8% had a higher risk of CVD (10-year risk of atherosclerotic CVD risk score (ASCVD) above 7.5%), and according to the European Society of Cardiology systemic coronary risk evaluation 2 (SCORE2), 71.7% had a high- to very high-risk rate. The agreement between various CVD risk tools varied, with Framingham heart study risk score (FRS), modified FRS, data collection on adverse effects of anti-HIV drugs (DAD), and SCORE2 for high-risk countries showing overall agreement rates of 82%, 94%, 91%, and 36%, respectively, compared to ASCVD. According to the 2021 European and 2019 American Cardiology guidelines, 75.3% and 47.1% of PLWH would be eligible for lipid-lowering agents, respectively. The diagnosed CVD prevalence highlighted the importance of monitoring cardiovascular health and comorbidities in this population. SCORE2 identified a greater number of individuals at high/very high risk compared to other prediction tools. The implementation of CVD prevention through lipid-lowering therapy was far from desired levels in our cohort.

Patient preferences for ocular hypertension monitoring: a discrete choice experiment

Background/aimsTo elicit the preferences and calculate the willingness to pay (WTP) of patients with ocular hypertension (OHT) for eye monitoring services in the UK.MethodsPatients with OHT aged at least 18...

A Comprehensive Model for Understanding Breast Cancer Screening Hesitancy: Integrating the Health Belief Model and the Confidence, Convenience, Complacency, Constraints, and Risk and Responsibility Calculations (5C) Model

A Comprehensive Model for Understanding Breast Cancer Screening Hesitancy: Integrating the Health Belief Model and the Confidence, Convenience, Complacency, Constraints, and Risk and Responsibility Calculations (5C) Model

Cerebral palsy characteristics in term-born children with and without detectable perinatal risk factors: A cross-sectional study.

To compare, in term-born children with cerebral palsy (CP), the characteristics of those who exhibit detectable risk factors for CP at birth with those who do not. This was a cross-sectional study of term-born children using the Canadian Cerebral Palsy Registry comparing those with and without perinatal risk factors and/or neonatal symptoms for pregnancy, birth and neonatal characteristics, magnetic resonance imaging (MRI) findings, CP subtype, and impairment severity. Risk factors were quantified with a CP risk calculator. Multivariable and multinomial regressions were expressed as odds ratios (OR) and relative risk ratios. Of 1333 term-born children, 781 (58.6%) had complete variables for the CP risk calculator, of whom 195 (25%) had 'undetectable' newborn infant CP risk, and they did not have greater postneonatal brain injury. Focal injury on MRI was more common (OR 2.0, 95% confidence interval [CI] 1.3-3.1) than in the 'detectable' group. The 'undetectable' group had more unilateral CP (OR 1.8, 95% CI 1.3-2.6), less severe motor impairment (OR 0.76, 95% CI 0.67-0.86), and were more verbal (OR 2.3, 95% CI 1.5-3.6). In the Canadian CP Registry, one-quarter of term-born children lacked neonatal encephalopathy, seizures, or perinatal risk factors. They were more likely to have unilateral CP, focal MRI findings, and communicate with words than children with risk factors or neonatal symptoms.

Overview of Finnish national patient data repository for research on medical risk assessment

The Kanta Patient Data Repository (PDR) contains healthcare data from the population of Finland for more than a decade. The repository is a continuously expanding real world dataset produced by many information systems and healthcare service providers. Kanta data has been available for secondary uses such as scientific research since 2019. The data can be requested from the Finnish authority Findata. However, before a request has been accepted, it is difficult to assess if the accumulated data allows answering a specific research question. Publicly available descriptions of data structures in the Kanta PDR do not tell how much they are used in practice. This publication enables future data use cases by providing a view on the overall availability of types of structured health data in the Kanta PDR based on a sample of 96 200 medical histories of over 18-year-old patients. We conclude that the Kanta PDR is a promising source of real world data for development and evaluation of medical risk calculators within the Finnish population. The wide coverage of the Finnish population and timeliness of the data are its strengths as a source of research data also outside of Finnish context. However, the limitations on data availability in variable level need to be considered on a case-by-case basis. Main challenges in the use of data in the Kanta PDR are multiple code systems for laboratory results, short durations of recorded data for specific data types, and missing or very rarely used structured format e.g., in cases of tobacco and alcohol use.

Including educational status may improve cardiovascular risk calculations such as SCORE2.

The association between education and atherosclerotic cardiovascular disease (ASCVD) has been well described for decades. Nevertheless, most cardiovascular risk models, including SCORE2, still do not take educational status into account even if this factor is easily assessed and costs nothing to acquire. Using carotid plaques as a proxy for ASCVD, we analysed educational status as associated with carotid plaque development, to determine if the relationship remains, how it relates to traditional risk factors and, how it impacts the European cardiovascular risk model, SCORE2. Our study also provides further data on plaque development in a well-characterised population nearly equally weighted by gender. 9,083 subjects (51% female, 49% male) from the Paracelsus 10,000 cohort, underwent a carotid doppler duplex as part of thorough screening for subclinical ASCVD. Well over 90% of carotid doppler duplex examinations were performed by the same experienced clinician. Subjects were then classified by educational status using the Generalized International Standard Classification of Education. Plaque absence or presence was dichotomised and variables analysed using regression modelling to examine educational status relative to cardiovascular risk factors and with respect to the SCORE2 model. Using medium educational status as a reference, subjects in our cohort with low educational status had higher odds, while subjects with high educational status had lower odds for carotid plaques compared to subjects with medium education (aOR 1.76 95%CI 1.50-2.06; and 0.0.63 95%CI 0.57-0.70, respectively). Even after adjusting for common risk factors including metabolic syndrome and SCORE2, the relationship was maintained. Furthermore, when comparing the potential predictive power of SCORE2 alone and plus educational status using the Akaike information criterion, we showed a 'better fit' when educational status was added. Measuring educational status is cost-free and easy for clinicians to obtain. We believe cardiovascular risk prediction models such as SCORE2 may more accurately reflect individual risk if educational status is also taken into account. Additionally, we believe clinicians need to understand and appropriately address educational status as a risk factor, to better quantify individual risk and take appropriate measures to reduce risk so that the association may finally be broken.

Clinical, biochemical, and densitometric profiles and FRAX risk calculations of South African patients with fragility fractures of the hip: observations from a tertiary care centre

Clinical, biochemical, and densitometric profiles and FRAX risk calculations of South African patients with fragility fractures of the hip: observations from a tertiary care centre

Comparison of heart failure risk assessment tools among cancer survivors

BackgroundCancer survivors have an increased risk of incident heart failure (HF) attributable to shared risk factors and cancer treatment-induced cardiac dysfunction. Selection for HF screening depends on risk assessment, but the optimal means of assessing risk is undefined. We undertook a comparison of HF risk calculators among survivors.MethodsIn this study from the UK Biobank, cancer and HF diagnoses were determined based on the International Classification of Diseases (ICD)-10 code and non-cancer participants were included as controls. Participants’ risk of incident HF was determined using the Heart Failure Association-International Cardio-oncology Society (HFA-ICOS), the Atherosclerosis Risk in Communities (ARIC-HF) and the Pooled Cohort Equations to Prevent Heart Failure (PCP-HF). The predictive performances of each were compared using the area under the curve (AUC).ResultsAfter propensity matching with age and sex, 9,232 survivors from breast cancer or lymphoma (mean age 59.9 years, 87.8% female), and 23,800 survivors from other cancer types (mean age 59.1 years, 85.8% female) were included in the analysis. The discriminative value for HFA-ICOS (AUC 0.753 [95%CI: 0.739–0.766]) and ARIC-HF (0.757 [95%CI: 0.744–0.770]) were similar, and superior to PCP-HF (0.717 [95%CI: 0.702–0.732]). The overall performance for each risk score was better among participants in other cancer types than those with breast cancer and lymphoma.ConclusionsHFA-ICOS and ARIC-HF outperformed the PCP-HF among cancer- and non-cancer cohort, although all showed modest discrimination for incident HF to be applied to clinical practice. A cancer-specific HF prediction tool could facilitate HF prevention among survivors.

Comparison of a Risk Calculator With Frailty Indices in Patients Undergoing Lung Cancer Resection.

While frailty has gained attention for its utility in risk stratification, no studies have directly compared them to existing risk calculators. The objective of this study was to compare the risk stratification of the American College of Surgeons Surgical Risk Calculator (ACS-SRC), the Revised Risk Analysis Index (RAI-rev), and the Modified Frailty Index (5-mFI). The primary outcomes were 30-day postoperative morbidity, 30-day postoperative mortality, unplanned readmission, unplanned reoperation, and discharge disposition other than home. Patients undergoing anatomic lung resection for primary, nonsmall cell lung cancer were identified within the ACS National Quality Improvement Program (ACS NSQIP) database. Tools were compared for discrimination in the primary outcomes. 9663 patients undergoing anatomic lung resection for cancer between 2012 and 2014 were included. The cohort was 53.1% female. Median age at diagnosis was 67 (IQR 59-74) years. Perioperative morbidity and mortality rates were 10.9% (n = 1048) and 1.6% (n = 158). Rates of 30-day postoperative unplanned readmission and reoperation were 7.5% (n = 725) and 4.8% (n = 468). The ACS-SRC had the highest discrimination for all measured outcomes, as measured by the area under the receiver operating curve (AUC) and corresponding confidence interval (95% CI). This included perioperative mortality (AUC 0.74, 95% CI 0.71-0.78), compared to RAI-rev (AUC 0.66, 95% CI 0.62-0.69) and 5-mFI (AUC 0.61, 95% CI 0.57-0.65; p < 0.001). The RAI-rev and 5-mFI had similar discrimination for all measured outcomes. ACS-SRC was the perioperative risk stratification tool with the highest predictive discrimination for adverse, 30-day, postoperative events for patients with cancer treated with anatomic lung resection.

Development and Validation of a 30-Day Point-Scoring Risk Calculator for Open Groin Vascular Surgery: The George Washington Groin Score

BackgroundOpen groin vascular surgeries are important in managing peripheral arterial diseases. Given its inherent risks and the diverse patient profiles, there is a need for risk assessment tools. This study aimed to develop a 30-d point-scoring risk calculator for patients undergoing open groin vascular surgeries. MethodsPatients underwent open groin vascular surgery, including aortobifemoral, axillofemoral, femorofemoral, iliofemoral, femoral-popliteal, and femoral-tibial bypass as well as thromboendarterectomy, were identified in American College of Surgeons National Surgical Quality Improvement Program database from 2005 to 2021. Patients were randomly sampled into experimental (2/3) and validation (1/3) groups. The George Washington (GW) groin score, a weighted point-scoring system, was developed for 30-d mortality from multivariable regression on preoperative risk variables by Sullivan's method. GW groin score was subjected to internal and external validation. Furthermore, the effectiveness of GW groin score was evaluated in 30-d major surgical complications. ResultsA total of 129,424 patients were analyzed, with 86,715 allocated to experimental group and 42,709 to validation group. GW groin score is derived as follows: aortobifemoral bypass (2 points), axillofemoral bypass (1 point), age (>75 y, 2 points; 65-75 y, 1 point), disseminated cancer (2 points), emergent presentation (1 point), American Society of Anesthesiology score 4 or 5 (1 point), dialysis (1 point), and preoperative sepsis (1 point).GW groin score exhibited robust discrimination (c-statistic = 0.794, 95% CI = 0.786-0.803) and calibration (Brier score = 0.029). The transition from individual preoperative variables (c-statistic = 0.809, 95% CI = 0.801-0.818) to the point-scoring system was successful and external validation of the score was confirmed (c-statistic = 0.789, 95% CI = 0.777-0.801, Brier score = 0.030). Furthermore, GW groin score can effectively discriminate major surgical complications. ConclusionsThis study developed GW groin score, a concise and comprehensive 10-point risk calculator. This well-validated score demonstrates robust discriminative and predictive abilities for 30-d mortality and major surgical complications following open groin vascular surgeries. GW groin score can anticipate potential perioperative complications and guide treatment decisions.

Development and Validation of the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk).

Objectives: The aim was to develop and validate the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk), aiding community healthcare workers in the early identification of individuals at high risk of mild cognitive impairment (MCI). Methods: Based on nationally representative community survey data, backward stepwise regression was employed to screen the variables, and logistic regression was utilized to construct the CGMCI-Risk. Internal validation was conducted using bootstrap resampling, while external validation was performed using temporal validation. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were employed to evaluate the CGMCI-Risk in terms of discrimination, calibration, and net benefit, respectively. Results: The CGMCI-Risk model included variables such as age, educational level, sex, exercise, garden work, TV watching or radio listening, Instrumental Activity of Daily Living (IADL), hearing, and masticatory function. The AUROC was 0.781 (95% CI = 0.766 to 0.796). The calibration curve showed strong agreement, and the DCA suggested substantial clinical utility. In external validation, the CGMCI-Risk model maintained a similar performance with an AUROC of 0.782 (95% CI = 0.763 to 0.801). Conclusions: CGMCI-Risk is an effective tool for assessing cognitive function risk within the community. It uses readily predictor variables, allowing community healthcare workers to identify the risk of MCI in older adults over a three-year span.

A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets

The airport apron is a high-risk area for aircraft collisions due to its heavy operational load and high aircraft density. Currently, existing quantitative models for apron collision risk provide limited consideration and classification of risk areas. In response, this paper proposes a Petri net-based method for assessing aircraft collision risk. The method predicts the probability of aircraft reaching different areas at different times based on operational data, enabling the calculation of collision risks within the Petri net framework. This approach highlights areas with potential collision risks and provides a classification evaluation. Subsequently, aircraft path re-planning is carried out to reduce collision risks. The model simplifies the complex operations of the apron system, making the calculation process clearer. The results show that, during the mid-phase of aircraft taxiing, there is a significant deviation between the actual and ideal positions of aircraft. Areas with high taxiway occupancy are more prone to collision risks. On peak days, due to relatively high flight volumes, the frequency of collision risks is 14% higher than on regular days, with an average risk increase of 23.3%, and the risks are more concentrated. Therefore, reducing collision risks through path planning becomes more challenging. It is recommended to focus attention on areas with high taxiway occupancy during peak periods and carefully plan routes to ensure apron safety.

Mapping Drought Disaster Risk Due to Climate Change in Kulon Progo District, Indonesia

Kulon Progo Regency is one of the regencies that always experiences severe drought. One of the factors that cause drought in Kulon Progo Regency is climate change due to global warming which causes changes in rainfall. The existence of drought certainly causes many problems in life due to the lack of water supply. Therefore, the purpose of this research is to conduct disaster risk mapping to obtain information on how to mitigate drought. The method used is to calculate the drought index using the Thornthwaite Matter method, calculate vulnerability based on BPS data, and calculate regional capacity based on the Regulation of BNBP Number 3 of 2012. After that, the calculation of disaster risk is then mapped. In this study, disaster risk is divided into two, disaster risk per year and dry months. The results of the annual disaster risk found that almost all sub-districts experienced low drought disaster risk, except for the Grimulyo sub-district which experienced moderate drought disaster risk. For the results of disaster risk in dry months, it is found that five sub-districts are classified into the low disaster risk category, namely Temon, Wates, Panjatan, Galur, and Nanggulan sub-districts. In addition, there are five sub-districts that fall into the medium disaster risk category, namely Lendah, Sentolo, Pengasih, Kokap, and Kalibawang. Finally, there are two sub-districts that fall into the high disaster risk category, namely Girimulyo and Samigaluh sub-districts.

Risk Assessment of UAV Cyber Range Based on Bayesian–Nash Equilibrium

In order to analyze the choice of the optimal strategy of cyber security attack and defense in the unmanned aerial vehicles’ (UAVs) cyber range, a game model-based UAV cyber range risk assessment method is constructed. Through the attack and defense tree model, the risk assessment method is calculated. The model of attack and defense game with incomplete information is established and the Bayesian–Nash equilibrium of mixed strategy is calculated. The model and method focus on the mutual influence of the actions of both sides and the dynamic change in the confrontation process. According to the calculation methods of different benefits of different strategies selected in the offensive and defensive game, the risk assessment and calculation of the UAV cyber range are carried out based on the probability distribution of the defender’s benefits and the attacker’s optimal strategy selection. An example is given to prove the feasibility and effectiveness of this method.

From Calculation to Communication: Using Risk Score Calculators to Inform Clinical Decision Making and Facilitate Patient Engagement.

Risk score calculators are a widely developed tool to support clinicians in identifying and managing risk for certain diseases. However, little is known about physicians' applied experiences with risk score calculators and the role of risk score estimates in clinical decision making and patient communication. Physicians providing care in outpatient community-based clinical settings (N = 20) were recruited to participate in semi-structured individual interviews to assess their use of risk score calculators in practice. Two study team members conducted an inductive thematic analysis using a consensus-based coding approach. Participants referenced at least 20 risk score calculators, the most common being the Atherosclerotic Cardiovascular Disease Risk Calculator. Ecological factors related to the clinical system (e.g., time), patient (e.g., receptivity), and physician (e.g., experience) influenced conditions and patterns of risk score calculator use. For example, compared with attending physicians, residents tended to use a greater variety of risk score calculators and with higher frequency. Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool. The degree to which risk score estimates influenced physician decision making and whether and how these scores were communicated to patients varied, reflecting a nuanced role of risk score calculator use in clinical practice. The theory of planned behavior can help explain how attitudes, beliefs, and norms shape the use of risk score estimates in clinical decision making and patient communication. Additional research is needed to evaluate best practices in the use of risk score calculators and risk score estimates. The risk score calculators and estimates that participants referenced in this study represented a range of conditions (e.g., heart disease, anxiety), levels of model complexity (e.g., probability calculations, scales of severity), and output formats (e.g., point estimates, risk intervals).Risk score calculators that are easily accessed, have simple inputs, and are trusted by physicians appear more likely to be used.Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool.Risk score estimates helped participants manage the uncertainty and complexity of various clinical situations, yet consideration of the limitations of these estimates was relatively minimal.Developers of risk score calculators should consider the patient- (e.g., response to risk score estimates) and physician- (e.g., training status) related characteristics that influence risk score calculator use in addition that of the clinical system.

Evaluation of novel cardiovascular risk calculators in patients with rheumatoid arthritis

Evaluation of novel cardiovascular risk calculators in patients with rheumatoid arthritis

UroARC: A novel surgical risk calculator for older adults undergoing pelvic organ prolapse and stress urinary incontinence surgery.

Surgeries for pelvic organ prolapse (POP) and stress urinary incontinence (SUI) are commonly performed in older adults, many of whom are also frail. A surgical risk calculator for older adults undergoing POP/SUI surgeries that incorporates frailty, a factor known to increase the risk of surgical complications, would be helpful for preoperative counseling but currently does not exist. Medicare Carrier, Outpatient, and MedPAR files were examined for beneficiaries undergoing POP and SUI surgery between 2014 and 2016. A total of 15 POP/SUI categories were examined. The Claims-Based Frailty Index (CFI), a validated measure of frailty in Medicare data, and Charlson Comorbidity Index were deconstructed into their individual variables, and individual variables were entered into stepwise logistic regression models to determine which variables were most highly predictive of 30-day complications and 1-year mortality. To verify the prognostic accuracy for each model for surgical complications of interest, calibration curves and tests of model fit, including C-statistic, Brier scores, and Spiegelhalter p values, were determined. In total, 108 479 beneficiaries were included. Among these, 4.7% had CFI scores consistent with mild to severe frailty (CFI≥0.25). A total of 13 prognostic variable categories were found to be most highly predictive of postoperative complications. Calibration curves for each outcome of interest showed models were well-fit. Most models demonstrated high c-statistic values (≥0.7) and high Spiegelhalter p values (≥0.9), indicating good model calibration and excellent discrimination, and low Brier scores (<0.02), indicating high model accuracy. Urologic surgery for older Adults Risk Calculator serves as a novel surgical risk calculator that is readily accessible to both patients and clinicians that specifically factors in components of frailty. Furthermore, this calculator accounts for the heterogeneity of an aging population and can assist in individualized surgical decision-making for these common procedures.

7459 Predictors of a Second Fracture in Men With Osteoporosis-related Fractures

Abstract Disclosure: R.H. Lee: None. Y. Wang: Employee; Self; Radius Health, Inc. S.A. Williams: Employee; Self; Star Biopharma, LLC. N. Pyrih: Employee; Self; Cobbs Creek Healthcare. B.H. Mitlak: Employee; Self; Radius Health, Inc. J.R. Curtis: Consulting Fee; Self; Amgen Inc, Eli Lilly &amp; Company, Radius Health, Inc, UCB. Grant Recipient; Self; Amgen Inc, Eli Lilly &amp; Company, Radius Health, Inc, UCB. Background: Management of osteoporosis (OP)-related fractures in men is suboptimal. Real-world evidence suggests that, after men sustain a first fracture, they experience a higher risk for subsequent fracture and greater mortality compared to women. There is limited knowledge regarding absolute risk and predictors of second fractures in men and fracture risk assessment tools may be less accurate in men (Vilaca et al 2022). The objective of this study was to develop a model to identify risk factors for a second fracture in men in the year following an initial fracture. Methods: Anonymized patient-level medical and pharmacy claims data from Symphony Health PatientSource® were used. The study criteria included males ≥40 years with a case-qualifying OP-related fracture between January 1, 2017 to May 31, 2021 based on a claims-based validated algorithm (Wright et al 2019). Logistic regression models with a binary outcome of a second fracture versus no second fracture within one year of index fracture were created to predict second fracture occurrence. The study built upon a previous model (Williams et al 2021) developed for OP-related fractures for both men and women incorporating additional variables relevant to men based on the current literature. A separate model to identify risk factors for a secondary nonvertebral fracture (NVF) in men was also developed. Results: Of 956,828 men identified with an index fracture (677,047 commercial; 279,781 Medicare Advantage), 13% (126,106) experienced a second fracture within 1 year following the index fracture. More than two thirds of secondary fractures were NVFs. Multivariable logistic regression showed that patients with an index fracture at the spine (OR [95% CI] 1.71 [1.68, 1.74]) or hip (OR [95% CI] 1.79 [1.75, 1.82]) were the most likely to have a second fracture within a year of index fracture. An index clinical spine fracture was not associated with an increased risk of secondary NVFs. Nonfracture spinal cord injury was included among the predictors with the greatest magnitude of estimate for NVF (OR [95% CI] 1.21 [1.08, 1.36]). In addition to fracture history, advanced age, history of falls, use of medications that increase fall risk, OP diagnosis, treatment with OP medication, low body weight, and excessive alcohol use were significant and the numerically largest predictors for a second fracture. Type 2 diabetes, but not GLP-1 use, also increased risk of secondary fracture. Variables negatively associated with future fractures included treatment history with alpha blockers (beta blockers in the Medicare cohort) and diuretics. Conclusion: Predictors of fracture in men were overall consistent with the literature (and risk factors identified in the FRAX risk calculator). Regardless of gender, recent fracture is always the highest risk factor for a secondary fracture. Presentation: 6/3/2024

UroARC: A Novel Surgical Risk Calculator for Older Adults Undergoing Suprapubic Tube Placement

ObjectivesTo develop a surgical risk calculator for older adults undergoing suprapubic tube (SPT) placement that specifically factors in frailty, a key predictor of surgical risk in this vulnerable and heterogenous population. MethodsMedicare MedPAR, Outpatient, and Carrier files for beneficiaries undergoing SPT placement between 2014-2016 were examined. The Claims-Based Frailty Index (CFI), a validated measure of frailty, was used to calculate baseline frailty for each beneficiary. Stepwise regression models were used for each variable within the CFI and Charlson Comorbidity Index to determine the variables most highly predictive of postoperative complications. The most highly predictive variables were then combined into parsimonious categories. To ensure the prognostic accuracy for each outcome, calibration curves and tests of model fit, including C-statistics, Brier scores, and Spiegelhalter p-values were calculated. ResultsA total of 26,999 beneficiaries were included. Among these, 39.1% were pre-frail, 36.8% were mildly frail, and 12.3% were moderately-to-severely frail. Thirteen prognostic variable categories were deemed highly predictive of postoperative complications of interest. All models demonstrated low Brier scores, indicating high model accuracy, and high C-statistic and Spiegelhalter p-values, consistent with excellent model discrimination and calibration, respectively. Excellent model fit was seen on calibration curves for each outcome. ConclusionsUroARC is a novel surgical tool for older adults undergoing SPT placement that specifically factors in frailty. This risk calculator has high accuracy, calibration, and discrimination, and serves as a valuable resource to patients and clinicians for those undergoing consideration for SPT placement.