Risk Assessment Score Research Articles

Limits of pre-endoscopic scoring systems in geriatric patients with upper gastrointestinal bleeding

Upper gastrointestinal bleeding (UGIB) is a common cause of hospital admission worldwide and several risk scores have been developed to predict clinically relevant outcomes. Despite the geriatric population being a high-risk group, age is often overlooked in the assessment of many risk scores. In this study we aimed to compare the predictive accuracy of six pre-endoscopic risk scoring systems in a geriatric population hospitalised with UGIB. We conducted a multi-center cross-sectional study and recruited 136 patients, 67 of these were 65–81.9 years old (“< 82 years”), 69 were 82–100 years old (“≥ 82 years”). We performed six pre-endoscopic risk scores very commonly used in clinical practice (i.e. Glasgow-Blatchford Bleeding and its modified version, T-score, MAP(ASH), Canada–United Kingdom–Adelaide, AIMS65) in both age cohorts and compared their accuracy in relevant outcomes predictions: 30-days mortality since hospitalization, a composite outcome (need of red blood transfusions, endoscopic treatment, rebleeding) and length of hospital stay. T-score showed a significantly worse performance in mortality prediction in the “≥ 82 years” group (AUROC 0.53, 95% CI 0.27–0.75) compared to “< 82 years” group (AUROC 0.88, 95% CI 0.77–0.99). In the composite outcome prediction, except for T-score, younger participants had higher sensitivities than those in the “≥ 82 years” group. All risk scores showed low performances in the prediction of length of stay (AUROCs ≤ 0.70), and, except for CANUKA score, there was a significant difference in terms of accuracy among age cohorts. Most used UGIB risk scores have a low accuracy in the prediction of clinically relevant outcomes in the geriatric population; hence novel scores should account for age or advanced age in their assessment.

Evaluation of Machine Learning Algorithms for Pressure Injury Risk Assessment in a Hospital with Limited IT Resources.

Clinical decision support systems for Nursing Process (NP-CDSSs) help resolve a critical challenge in nursing decision-making through automating the Nursing Process. NP-CDSSs are more effective when they are linked to Electronic Medical Record (EMR) Data allowing for the computation of Risk Assessment Scores. Braden scale (BS) is a well-known scale used to identify the risk of Hospital-Acquired Pressure Injuries (HAPIs). While BS is widely used, its specificity for identifying high-risk patients is limited. This study develops and evaluates a Machine Learning (ML) model to predict the HAPI risk, leveraging EMR readily available data. Various ML algorithms demonstrated superior performance compared to BS (pooled model AUC/F1-score of 0.85/0.8 vs. AUC of 0.63 for BS). Integrating ML into NP-CDSSs holds promise for enhancing nursing assessments and automating risk analyses even in hospitals with limited IT resources, aiming for better patient safety.

Application of Motivational Interviewing in climate-sensitive health counselling – A workshop report

IntroductionThis workshop report explores the application of Motivational Interviewing (MI) in Climate-sensitive Health Counselling (CSHC) within the context of primary health care. As there is a growing interest in the connection of individual health and climate change mitigation, we provide practical guidance on integrating MI techniques in CSHC. MethodsIn June 2023, a 2-day workshop on MI was conducted at the Institute of General Medicine, University of Cologne. The workshop, facilitated by a certified MI trainer, combined theoretical content with practical group exercises. In a second workshop the staff of the Institute of General Medicine discussed the application of MI in CSHC. ResultsWe identified and specified five tools to apply MI in CSHC: A) risk assessment scores, B) relevance assessment scales, C) decision matrices, D) confidence assessment scales, and E) SMART goals. These tools provide practical insights for integrating MI into primary care consultations, offering a time-efficient approach to CSHC. DiscussionOur results present a promising approach for healthcare professionals to incorporate climate-related aspects into health counselling of patients. Feasibility and effects of MI in CSHC are still unclear and require further research. ConclusionThe tools identified provide practical guidance for the application of MI in climate-sensitive health counselling (CSHC) as well as guidance on conducting appropriate studies.

Sharma's risk assessment score as a decision making tool to decide between total hip arthroplasty or hemiarthroplasty as a treatment option for elderly patients with fracture neck of femur. A prospective observational study

PurposeThe decision to do a total hip arthroplasty (THA) or a hemiarthroplasty (HA) in an elderly with a fracture neck of femur (FNOF) is commonly based upon the surgeon's preference, pre-anesthetic fitness, hospital setup, and intensive care backup. The author devised a Sharma's risk assessment score (SRAS), based upon all the common factors that affect the surgical outcomes following FNOF in the elderly, to help orthopedic surgeons decide between THA or HA as a treatment for FNOF. Material & methodsIt was a prospective observational study conducted in a tertiary-level institute. SRAS is based upon 10 parameters with each parameter having a max score of 4 and a minimum score of 1. So the maximum score a patient can score is 40 and the minimum a patient can score is 10. It was hypothesized that a patient with FNOF with a preop SRAS score >20 if subjected to a (HA) and a patient with a preop SRAS score ≤ 20 if subjected to a (THA) would have better outcomes and low complication rates. Out of Eighty-eight patients with FNOF, 7 were lost to follow-up. The remaining 81 patients with FNOF were prospectively followed between May 2018 and May 2022 and segregated into two groups THA (n = 47) and HA(n = 34) based on the SRAS. ResultsThe average length of follow-up was 2.6 years (6 months–4 years). The average SRAS was an average 25.7(21–32) in the HA group and 16.2 (11–20) in the THA group. Complications were seen in 12.7 % of the THA group and 17.6 % of the HA group. We had a 2.1 % 90-day mortality in the THA group and a 2.9 % 90-day mortality in the HA group. One year mortality in THA was 2.1 % and it was increased to 5.8 % in the HA group. ConclusionSRAS is a useful decision-making tool that would guide surgeons to decide between THA or HA as a treatment option for elderly FNOF and would help minimize post-operative complications and reduce mortality. Level 3 studyProspective observational study.

Investigation of bias in the automated assessment of school violence

ObjectivesNatural language processing and machine learning have the potential to lead to biased predictions. We designed a novel Automated RIsk Assessment (ARIA) machine learning algorithm that assesses risk of violence and aggression in adolescents using natural language processing of transcribed student interviews. This work evaluated the possible sources of bias in the study design and the algorithm, tested how much of a prediction was explained by demographic covariates, and investigated the misclassifications based on demographic variables. MethodsWe recruited students 10–18 years of age and enrolled in middle or high schools in Ohio, Kentucky, Indiana, and Tennessee. The reference standard outcome was determined by a forensic psychiatrist as either a “high” or “low” risk level. ARIA used L2-regularized logistic regression to predict a risk level for each student using contextual and semantic features. We conducted three analyses: a PROBAST analysis of risk in study design; analysis of demographic variables as covariates; and a prediction analysis. Covariates were included in the linear regression analyses and comprised of race, sex, ethnicity, household education, annual household income, age at the time of visit, and utilization of public assistance. ResultsWe recruited 412 students from 204 schools. ARIA performed with an AUC of 0.92, sensitivity of 71%, NPV of 77%, and specificity of 95%. Of these, 387 students with complete demographic information were included in the analysis. Individual linear regressions resulted in a coefficient of determination less than 0.08 across all demographic variables. When using all demographic variables to predict ARIA’s risk assessment score, the multiple linear regression model resulted in a coefficient of determination of 0.189. ARIA performed with a lower False Negative Rate (FNR) of 15.2% (CI [0 – 40]) for the Black subgroup and 12.7%, CI [0 – 41.4] for Other races, compared to an FNR of 26.1% (CI [14.1 – 41.8]) in the White subgroup. ConclusionsBias assessment is needed to address shortcomings within machine learning. In our work, student race, ethnicity, sex, use of public assistance, and annual household income did not explain ARIA’s risk assessment score of students. ARIA will continue to be evaluated regularly with increased subject recruitment.

CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease.

The global prevalence of cardiovascular diseases (CVDs) as a leading cause of death highlights the imperative need for refined risk assessment and prognostication methods. The traditional approaches, including the Framingham Risk Score, blood tests, imaging techniques, and clinical assessments, although widely utilized, are hindered by limitations such as a lack of precision, the reliance on static risk variables, and the inability to adapt to new patient data, thereby necessitating the exploration of alternative strategies. In response, this study introduces CardioRiskNet, a hybrid AI-based model designed to transcend these limitations. The proposed CardioRiskNet consists of seven parts: data preprocessing, feature selection and encoding, eXplainable AI (XAI) integration, active learning, attention mechanisms, risk prediction and prognosis, evaluation and validation, and deployment and integration. At first, the patient data are preprocessed by cleaning the data, handling the missing values, applying a normalization process, and extracting the features. Next, the most informative features are selected and the categorical variables are converted into a numerical form. Distinctively, CardioRiskNet employs active learning to iteratively select informative samples, enhancing its learning efficacy, while its attention mechanism dynamically focuses on the relevant features for precise risk prediction. Additionally, the integration of XAI facilitates interpretability and transparency in the decision-making processes. According to the experimental results, CardioRiskNet demonstrates superior performance in terms of accuracy, sensitivity, specificity, and F1-Score, with values of 98.7%, 98.7%, 99%, and 98.7%, respectively. These findings show that CardioRiskNet can accurately assess and prognosticate the CVD risk, demonstrating the power of active learning and AI to surpass the conventional methods. Thus, CardioRiskNet's novel approach and high performance advance the management of CVDs and provide healthcare professionals a powerful tool for patient care.

Evaluation of the incidence, predictors, risk assessment scores and outcomes of thromboembolism in a cohort of Egyptian NHL patients - Real World Experience

Non-Hodgkin’s Lymphoma (NHL) is the most common subtype of lymphoma. The incidence of venous thromboembolism (VTE) in aggressive NHL was estimated recently to be 11%. Several risk assessment scores and factors are available to help identify cancer patients at risk for developing VTE. Patients with a pathologically confirmed diagnosis of NHL were identified at the Oncology Center of Mansoura University. The study included 777 patients: 719 with DLBCL-NOS, 26 with Anaplastic-B-cell, and 32 with T-cell-rich-NHL. Data were retrospectively collected from electronic medical records, including clinical, radiological, and laboratory information related to VTE and NHL. The median age at NHL diagnosis was 53 years, (range: 18–98). There was a male predominance, 51.4% of the cases. At initial lymphoma diagnosis, VTE was identified in 46 (5.9%) patients, and 61 (7.9%) patients experienced VTE while undergoing chemotherapy. According to logistic regression analysis, a PS (performance status) ≥ 2, bulky lesions, and mediastinal masses were significant predictors of VTE at presentation, with P-values of 0.022, 0.002, and < 0.001, respectively. Meanwhile, NHL patients who developed VTE during chemotherapy had significantly poorer PS, higher absolute neutrophilic counts (ANC), neutrophil/lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and lactate dehydrogenase (LDH) levels than lymphoma patients without VTE, with P-values of 0.003, 0.034, 0.049, 0.01 and 0.007, respectively, as determined by multivariate analysis. The ROC curve identified the cut-off values of 4.875 × 109/L for ANC, 2.985 for NLR, 144.85 for PLR, and 417.5 U/L for LDH as potential markers for predicting VTE in NHL patients. Patients with a PS ≥ 2 and values exceeding these cut-offs for ANC, NLR, and PLR experienced significantly higher incidences of VTE than other groups, with P-values of 0.003, < 0.001, < 0.001, and < 0.001, respectively. At the end of the follow-up, the overall survival was significantly shortened by VTE occurring during chemotherapy, hypoalbuminemia, intermediate-high and high international prognostic index (IPI) scores (intermediate-high and high), responses other than CR and relapse, all with P-values < 0.05. ECOG PS and Inflammatory markers such as NLR, PLR, and neutrophilic count could serve as predictors of the development of thrombotic events in patients with NHL-DLBCL. Additionally, the occurrence of VTE during chemotherapy is an independent poor prognostic marker for overall survival (OS).

Ability of an obstetric hemorrhage risk assessment tool to predict quantitative peripartum blood loss.

Hemorrhage risk assessment tools have been studied using estimated blood loss. We study the association between peripartum hemorrhage risk assessment score and peripartum quantified blood loss (QBL) in term vaginal and cesarean deliveries. This is a retrospective analysis conducted on 3,657 patients who underwent term vaginal and cesarean deliveries at a public hospital in New York City. Utilizing the risk assessment tool developed by the Association of Women's Health, Obstetric and Neonatal Nurses (AWHONN), patients were categorized into low-, medium-, or high-risk groups for postpartum hemorrhage. Medium-risk (B=0.08, SE=0.01, p<0.001) and high-risk (B=0.12, SE=0.02, p<0.001) AWHONN scores were associated with significantly higher QBL as compared to low-risk AWHONN score. Medium-risk approached significance (OR: 1.67, 95 % CI: 1.00, 2.79, p=0.050) and high-risk AWHONN score was significantly associated (OR: 1.95, 95 % CI: 1.09, 3.48, p=0.02) with increased odds for postpartum hemorrhage (≥1,000 mL). Each individual factor comprising the AWHONN score whose percentage in our sample was seen in greater than 2.7 % of patients was independently significantly associated with increased QBL (six of nine factors) and postpartum hemorrhage (four of nine factors). The AWHONN measure previously validated with estimated blood loss predicted obstetric blood loss with QBL. Although not on the basis of the data shown in our study, we believe that QBL should be routinely used to measure obstetric blood loss.

Assessment of Cardiovascular Risk and Factors among Post COVID-19 Patients with Type 2 Diabetes Mellitus and Dyslipidaemia

Background: The current study aims to assess the impact of structured patient education on cardiovascular complications in post-covid patients with type-2 diabetes mellitus and dyslipidaemia. Objectives: To assess the prevalence of cardiovascular risk among type-2 diabetes mellitus with dyslipidaemia in post-covid patients. to analyse the cardiovascular risk correlated with diabetes and lipid profile. to assess the impact of patient education on cardiovascular risk complications. Method: A total of 300 patients were screened and of them, 205 were enrolled in the study based on the inclusion and exclusion criteria. Patient education was given to selected patients using structured patient leaflets and oral education after baseline. They are followed up after 2 months. Cardiovascular risk was assessed by ASCVD and Framingham risk assessment score. Results: The study analysed that there was a high prevalence in males, the age group 51-70 years, non-alcoholic, and non-smokers. At the baseline, most patients were at high risk. On comparing the baseline with the review, a 49.3% difference was seen in the high-risk category. Conclusion: The study revealed that age, gender, low density lipoprotein, total cholesterol, and random blood sugar were the important cardiovascular risk factors. The implementation of pharmacist care in collaboration with physicians and nurses may reduce cardiovascular risk complications and help in better medication management.

Promoting financial inclusion for SMEs: Leveraging AI and data analytics in the banking sector

Financial inclusion is crucial for the growth and sustainability of small and medium-sized enterprises (SMEs), which are significant contributors to economic development and job creation. However, traditional banking models often fall short in serving the unique needs of SMEs due to perceived high risks and insufficient credit history. Leveraging artificial intelligence (AI) and data analytics, the banking sector can transform its approach to financial inclusion, offering tailored financial products and services that cater specifically to SMEs. AI and data analytics enable banks to analyze vast amounts of data from diverse sources, including transactional histories, social media activity, and market trends. This holistic view of an SME's financial health and business potential allows for more accurate risk assessment and credit scoring. Machine learning algorithms can identify patterns and predict creditworthiness, enabling banks to extend credit to SMEs that may have been overlooked by traditional models. Additionally, AI-driven insights facilitate the development of customized financial products, such as flexible loan terms and dynamic interest rates, that align with the cash flow cycles and operational realities of SMEs. Automated processes and AI-powered chatbots enhance customer service, providing SMEs with timely support and financial advice, thereby improving their banking experience. Data analytics also play a critical role in fraud detection and prevention, ensuring the security of transactions and building trust among SME clients. By continuously monitoring and analyzing transaction data, banks can quickly identify and mitigate fraudulent activities, protecting SMEs from financial losses. Moreover, AI and data analytics support the creation of financial literacy programs tailored to the specific needs of SMEs, empowering business owners with the knowledge and tools to make informed financial decisions. This educational aspect is vital in fostering a sustainable financial ecosystem for SMEs. In conclusion, the integration of AI and data analytics in the banking sector holds significant promise for promoting financial inclusion among SMEs. By providing more accessible, customized, and secure financial services, banks can support the growth and success of SMEs, ultimately contributing to broader economic development and financial stability.

The Cancer of the Bladder Risk Assessment Score and Mortality-Survival Relationship Among Patients Who Have Undergone Radical Cystectomy in the Turkish Urooncology Association Database

The Cancer of the Bladder Risk Assessment Score and Mortality-Survival Relationship Among Patients Who Have Undergone Radical Cystectomy in the Turkish Urooncology Association Database

Evaluating the performance of a risk assessment score tool to predict HIV acquisition among pregnant and postpartum women in Kenya.

Clinical risk score tools require validation in diverse settings and populations before they are widely implemented. We aimed to externally validate an HIV risk assessment tool for predicting HIV acquisition among pregnant and postpartum women. In the context of prevention of mother-to-child transmission programs, risk score tools could be used to prioritize retesting efforts and delivery of pre-exposure prophylaxis (PrEP) to pregnant and postpartum women most at risk for HIV acquisition while minimizing unnecessary perinatal exposure. Data from women enrolled in a cross-sectional study of programmatic HIV retesting and/or receiving maternal and child health care services at five facilities in Western Kenya were used to validate the predictive ability of a simplified risk score previously developed for pregnant/postpartum women. Incident HIV infections were defined as new HIV diagnoses following confirmed negative or unknown status during pregnancy. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC) and Brier score. Among 1266 women with 35 incident HIV infections, we found an AUC for predicting HIV acquisition of 0.60 (95% CI, 0.51, 0.69), with a Brier score of 0.27. A risk score >6 was associated with a 2.9-fold increase in the odds of HIV acquisition (95% CI, 1.48, 5.70; p = 0.002) vs scores ≤6. Women with risk scores >6 were 27% (346/1266) of the population but accounted for 52% of HIV acquisitions. Syphilis, age at sexual debut, and unknown partner HIV status were significantly associated with increased risk of HIV in this cohort. The simplified risk score performed moderately at predicting risk of HIV acquisition in this population of pregnant and postpartum women and may be useful to guide PrEP use or counseling.

DNA ploidy and PTEN as biomarkers for predicting aggressive disease in prostate cancer patients under active surveillance

BackgroundCurrent risk stratification tools for prostate cancer patients under active surveillance (AS) may inadequately identify those needing treatment. We investigated DNA ploidy and PTEN as potential biomarkers to predict aggressive disease in AS patients.MethodsWe assessed DNA ploidy by image cytometry and PTEN protein expression by immunohistochemistry in 3197 tumour-containing tissue blocks from 558 patients followed in AS at a Norwegian local hospital. The primary endpoint was treatment, with treatment failure (biochemical recurrence or initiation of salvage therapy) as the secondary endpoint.ResultsThe combined DNA ploidy and PTEN (DPP) status at diagnosis was associated with treatment-free survival in univariable- and multivariable analysis, with a HR for DPP-aberrant vs. DPP-normal tumours of 2.12 (p < 0.0001) and 1.94 (p < 0.0001), respectively. Integration of DNA ploidy and PTEN status with the Cancer of the Prostate Risk Assessment (CAPRA) score improved risk stratification (c-index difference = 0.025; p = 0.0033). Among the treated patients, those with DPP-aberrant tumours exhibited a significantly higher likelihood of treatment failure (HR 2.01; p = 0.027).ConclusionsDNA ploidy and PTEN could serve as additional biomarkers to identify AS patients at increased risk of developing aggressive disease, enabling earlier intervention for nearly 50% of the patients that will eventually receive treatment with current protocol.

Genetics in relation to cardiac diseases: Implications for general practitioners.

Cardiovascular diseases (CVDs) pose significant global health challenges, with genetics increasingly recognised as a key factor alongside traditional risk factors. This presents an opportunity for general practitioners (GPs) to refine their approaches. This article explores the impact of genetics on CVDs and its implications for GPs. It discusses monogenic disorders like inherited cardiomyopathies and polygenic risks, as well as pharmacogenetics, aiming to enhance risk assessment and personalised care. Monogenic disorders, driven by single gene mutations, exhibit predictable inheritance patterns, including inherited cardiomyopathies and channelopathies such as Long QT syndrome. Polygenic risks involve multiple genetic variants influencing CVD susceptibility, addressed through polygenic risk scores for precise risk assessment. Pharmacogenetics tailor drug interventions based on genetic profiles, though challenges like accessibility and ethical considerations persist. Integrating genetics into cardiovascular care holds promise for alleviating the global CVD burden and improving patient outcomes.

A comparison between risk assessment scores in prostate cancer

A comparison between risk assessment scores in prostate cancer

Venous and Arterial Thromboembolism in Lung Cancer Patients: A Retrospective Analysis.

Background: Patients with lung cancer face an increased incidence of venous (VTE) and arterial (ATE) thromboembolism. Risk factors for thrombosis remain unclear, particularly the impact of the use of immune checkpoint inhibitors (ICIs). We sought to compare the incidence of VTE and ATE in lung cancer patients receiving platinum-based therapy versus those receiving ICIs alone or in combination with chemotherapy and to validate the Khorana risk score for predicting VTE in the era of ICIs. Methods: A retrospective single-institution data analysis of 173 patients diagnosed with locally advanced or metastatic lung cancer at the Jena University hospital between 2015 and 2021. Results: The study revealed a high incidence of VTE (17.9%) and ATE (5.8%). The VTE risk was higher in patients diagnosed with adenocarcinoma (OR 0.29, 95% CI 0.09-0.93) than in patients with other histological types. A prior venous event was associated with an increased risk of recurrent VTE (OR 4.46, 95% CI 1.20-16.63). The incidence of thrombosis under first-line platinum-based chemotherapy did not differ from the incidence under ICIs (p = 0.19). There were no differences in the subgroup of patients who received ICIs alone or combined immunochemotherapy (p = 0.43). The Khorana score failed to predict the risk of VTE correctly. Conclusions: We did not find evidence supporting the theory that ICI therapy (alone or combined) increases the risk of thrombotic events. Adenocarcinoma and a prior history of VTE were strongly associated with an increased risk of VTE. Other scores for thrombotic risk assessment in lung cancer patients should be tested in prospective studies.

Mitigasi Risiko pada Proses Produksi Tahu Menggunakan Pendekatan Metode Failure Mode and Effect Analysis dan Risk Priority Number

The Bantul Tofu Industry is a medium-sized industry that processes soybeans into tofu located in the Bantul area. In the production process there are still errors that cause the tofu product to be damaged. The aim of this research is identify risks that occur in tofu production, analyze priority risk events, and make recommendations for improvements. The method used is FMEA to analyze the occurrence of risks or failures in a process and RPN to provide a risk assessment score for proposed improvements. Based on the research results, it was found that there were 14 total risk events and there were 5 risks that were mitigation priorities, such as spilling of tofu coconut milk, mixing of soybean quality, incorrect cutting of tofu, slippery production floors, and the presence of liquid waste. Recommendations for improvement are creating SOP, using adequate equipment, maintaining the cleanliness of production areas and routine maintenance. Keywords: Failure Mode and Effect Analysis, Risk Priority Number, Tofu Processing

The Utility of Machine Learning for Cardiology and Cardiac Surgery Risk Assessment Scores

Artificial intelligence (AI) has attracted great interest in the world of cardiology and cardiovascular surgery. For simplicity, AI has 3 distinct sectors: machine learning (ML), deep learning, and generative AI. In the case of ML, when calculating cardiovascular risk scores, ML algorithms analyze large, complex datasets (data mining) to predict the risk of morbidity and mortality.

The impact of testosterone-lowering medication on recidivism in individuals convicted of sexual offenses.

Treatment of individuals who have committed sexual offences with Testosterone-Lowering Medication (TLM) is a comparatively intrusive kind of intervention, which regularly takes place in coercive contexts. Thus, the question of efficacy, but also the question of who should be treated, when and for how long, are of great importance. Recidivism rates of TLM-treated high-risk individuals (+TLM; n = 54) were compared with high-risk individuals treated with psychotherapy only in the same forensic outpatient clinic (-TLM; n = 79). Group differences suggested a higher initial risk of + TLM (e.g. higher ris-assessment, previous convictions). Despite the increased risk, after an average time at risk of six years, +TLM recidivated significantly less often and significantly later than - TLM (27.8% vs. 51.9%). Such an effect was also found for violent (1.9% vs. 15.2%), but not for sexual (5.6% vs. 10.1%) and serious recidivism (5.6% vs. 10.1%), which could be explained partly by the small number of cases. In the course of treatment, TLM proved to be a significant variable for a positive process, whereas a high risk-assessment score indicated a rather negative course. In total, n = 19 individuals had stopped their TLM treatment, of these 31.6% recidivated. The results support the efficacy of TLM, particularly in the group of high-risk offenders.

The association between overuse and musculoskeletal injuries and the female athlete triad in Division I collegiate athletes.

Although the female athlete triad (Triad) has been associated with increased risk of bone-stress injuries (BSIs), limited research among collegiate athletes has addressed the associations between the Triad and non-BSI injuries. To elucidate the relationship between Triad and both BSI and non-BSI in female athletes. Retrospective cohort study. Primary and tertiary care student athlete clinic. National Collegiate Athletic Association Division I female athletes at a single institution. Participants completed a pre-participation questionnaire and dual-energy x-ray absorptiometry, which was used to generate a Triad cumulative risk assessment score (Triad score). The number of overuse musculoskeletal injuries that occurred while the athletes were still competing collegiately were identified through chart review. BSI and non-BSI were treated as count variables. The association between BSI, non-BSI, and Triad score was measured using Poisson regression to calculate rate ratios. Of 239 athletes, 43% of athletes (n = 103) sustained at least one injury. Of those, 40% (n = 95) sustained at least one non-BSI and 10% (n = 24) sustained at least one BSI over an average follow-up 2.5 years. After accounting for sport type (non-lean, runner, other endurance sport, or other lean advantage sport) and baseline age, we found that every additional Triad score risk point was associated with a significant 17% increase in the rate of BSI (rate ratio [RR] 1.17, 95% confidence interval [CI] 1.03-1.33; p = .016). However, Triad score was unrelated to non-BSI (1.00, 95% CI 0.91-1.11; p = .99). Compared with athletes in non-lean sports (n = 108), athletes in other lean advantage sports (n = 30) had an increased rate of non-BSI (RR: 2.09, p = .004) whereas distance runners (n = 46) had increased rates of BSI (RR: 7.65, p < .001) and non-BSI (RR: 2.25, p < .001). Higher Triad score is associated with an increased risk of BSI but not non-BSI in collegiate athletes.