Clinical Risk Management: As Modern Tool for Prevention and Management of Care and Prevention Occupational Risk

Clinical Risk Management aims to improve the performance quality of healthcare services through procedures that identify and prevent circumstances that could expose both the patient and the healthcare personnel to risk of an adverse event [...]

Comparison of Mammography Artificial Intelligence Algorithms for 5-year Breast Cancer Risk Prediction

PURPOSE: To examine the ability of 5 artificial intelligence (AI)-based computer vision algorithms, most trained to detect visible breast cancer on mammograms, to predict future risk relative to the Breast Cancer Surveillance Consortium clinical risk prediction model (BCSC v2). PATIENTS AND METHODS: In this case-cohort study, women who had a screening mammogram in 2016 at Kaiser Permanente Northern California with no evidence of cancer on final imaging assessment were followed through September 2021. Women with prior breast cancer or a known highly penetrant gene mutation were excluded. From the 329,814 total eligible women, a random subcohort of 13,881 women (4.2%) were selected, of whom 197 had incident cancer. All 4,475 additional incident cancers were also included. Continuous AI-predicted scores were generated from the index 2016 mammogram. Risk estimates were generated with the Kaplan-Meier method and time-varying area under the curve [AUC(t)]. RESULTS: For incident cancers at 0-1 year (interval cancer risk), BCSC demonstrated an AUC(t) of 0.62 (95% CI, 0.58-0.66), and the AI algorithms had AUC(t)s ranging from 0.66-0.71, all significantly higher than BCSC (P < .05). For incident cancers at 1 to 5 years (5-year future cancer risk), BCSC demonstrated an AUC(t) of 0.61 (95% CI, 0.60-0.62), and the AI algorithms had AUC(t)s ranging from 0.63 to 0.67, all significantly higher than BCSC. Combined BCSC and AI models demonstrated AUC(t)s for interval cancer risk of 0.67-0.73 and for 5-year future cancer risk of 0.66-0.68. CONCLUSION: The AI mammography algorithms we evaluated had significantly higher discrimination than the BCSC clinical risk model for interval and 5-year future cancer risk. Combined AI and BCSC models had slightly higher discrimination than AI alone.

Glucocorticoids Increase Fracture Risk and Fracture Prevalence Independently from Bone Mineral Density and Clinical Risk Factors: Results from the Gliwice Osteoporosis (GO) Study

The aim of the study was to establish the influence of glucocorticoids (GC) on fracture risk, probability, and prevalence. A set of 1548 postmenopausal women were divided into study group – treated with GC (n=114, age 66.48±7.6 years) and controls (n=1434, age 66.46±6.83 years). Data on clinical risk factors for osteoporosis and fractures were collected. Hip bone densitometry was performed using a device Prodigy (GE, USA). Fracture probability was established by FRAX, and fracture risk by Garvan algorithm and POL-RISK. Fracture risk and fracture probability were significantly greater for GC-treated women in comparison to controls. In the study group, there were 24, 3, 24, and 6 fractures noted at spine, hip, forearm, and arm, respectively. The respective numbers of fractures reported in controls at those skeletal sites were: 186, 23, 240, and 25. The use of GCs increased significantly prevalence of all major, spine and arm fractures. Also the number of all fractures was affected by GC use. Following factors significantly increased fracture probability: age (OR 1.04 per each year; 95% CI: 1.03–1.06), GC use (OR 1.54; 95% CI: 1.03–2.31), falls (OR 2.09; 95% CI: 1.60–2.73), and FN T-score (OR 0.62 per each unit; 95% CI: 0.54–0.71). In conclusion, in patients treated with GCs the fracture risk, probability, and prevalence were increased. This effect was evident regardless of whether GC therapy is included in the algorithm as a risk factor (FRAX, POL-RISK) or not taken into consideration (Garvan nomogram).

From Syndemic Lesson after COVID-19 Pandemic to a “Systemic Clinical Risk Management” Proposal in the Perspective of the Ethics of Job Well Done

The syndemic framework proposed by the 2021–2030 World Health Organization (WHO) action plan for patient safety and the introduction of enabling technologies in health services involve a more effective interpretation of the data to understand causation. Based on the Systemic Theory, this communication proposes the “Systemic Clinical Risk Management” (SCRM) to improve the Quality of Care and Patient Safety. This is a new Clinical Risk Management model capable of developing the ability to observe and synthesize different elements in ways that lead to in-depth interventions to achieve solutions aligned with the sustainable development of health services. In order to avoid uncontrolled decision-making related to the use of enabling technologies, we devised an internal Learning Algorithm Risk Management (LARM) level based on a Bayesian approach. Moreover, according to the ethics of Job Well Done, the SCRM, instead of giving an opinion on events that have already occurred, proposes a bioethical co-working because it suggests the best way to act from a scientific point of view.

Polygenic Risk Score Prediction for Endometriosis

Endometriosis is a major health care challenge because many young women with endometriosis go undetected for an extended period, which may lead to pain sensitization. Clinical tools to better identify candidates for laparoscopy-guided diagnosis are urgently needed. Since endometriosis has a strong genetic component, there is a growing interest in using genetics as part of the clinical risk assessment. The aim of this work was to investigate the discriminative ability of a polygenic risk score (PRS) for endometriosis using three different cohorts: surgically confirmed cases from the Western Danish endometriosis referral Center (249 cases, 348 controls), cases identified from the Danish Twin Registry (DTR) based on ICD-10 codes from the National Patient Registry (140 cases, 316 controls), and replication analysis in the UK Biobank (2,967 cases, 256,222 controls). Patients with adenomyosis from the DTR (25 cases) and from the UK Biobank (1,883 cases) were included for comparison. The PRS was derived from 14 genetic variants identified in a published genome-wide association study with more than 17,000 cases. The PRS was associated with endometriosis in surgically confirmed cases [odds ratio (OR) = 1.59, p = 2.57× 10−7] and in cases from the DTR biobank (OR = 1.50, p = 0.0001). Combining the two Danish cohorts, each standard deviation increase in PRS was associated with endometriosis (OR = 1.57, p = 2.5× 10−11), as well as the major subtypes of endometriosis; ovarian (OR = 1.72, p = 6.7× 10−5), infiltrating (OR = 1.66, p = 2.7× 10−9), and peritoneal (OR = 1.51, p = 2.6 × 10−3). These findings were replicated in the UK Biobank with a much larger sample size (OR = 1.28, p &lt; 2.2× 10−16). The PRS was not associated with adenomyosis, suggesting that adenomyosis is not driven by the same genetic risk variants as endometriosis. Our results suggest that a PRS captures an increased risk of all types of endometriosis rather than an increased risk for endometriosis in specific locations. Although the discriminative accuracy is not yet sufficient as a stand-alone clinical utility, our data demonstrate that genetics risk variants in form of a simple PRS may add significant new discriminatory value. We suggest that an endometriosis PRS in combination with classical clinical risk factors and symptoms could be an important step in developing an urgently needed endometriosis risk stratification tool.