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- Effect of C282Y genotype on self-reported musculoskeletal complications in hereditary hemochromatosisPublication . Brito Camacho, António; Funck-Brentano, Thomas; Simão, Márcio; Cancela, Leonor; Ottaviani, Sebastien; Cohen-Solal, Martine; Richette, PascalObjective Arthropathy that mimics osteoarthritis (OA) and osteoporosis (OP) is considered a complication of hereditary hemochromatosis (HH). We have limited data comparing OA and OP prevalence among HH patients with different hemochromatosis type 1 (HFE) genotypes. We investigated the prevalence of OA and OP in patients with HH by C282Y homozygosity and compound heterozygosity (C282Y/H63D) genotype. Methods A total of 306 patients with HH completed a questionnaire. Clinical and demographic characteristics and presence of OA, OP and related complications were compared by genotype, adjusting for age, sex, body mass index (BMI), current smoking and menopausal status. Results In total, 266 of the 306 patients (87%) were homozygous for C282Y, and 40 (13%) were compound heterozygous. The 2 groups did not differ by median age [60 (interquartile range [IQR] 53 to 68) vs. 61 (55 to 67) years, P=0.8], sex (female: 48.8% vs. 37.5%, P=0.18) or current smoking habits (12.4% vs. 10%, P=0.3). As compared with compound heterozygous patients, C282Y homozygous patients had higher median serum ferritin concentration at diagnosis [1090 (IQR 610 to 2210) vs. 603 (362 to 950) mu g/L, P<0.001], higher median transferrin saturation [80% (IQR 66 to 91%) vs. 63% (55 to 72%), P<0.001]) and lower median BMI [24.8 (22.1 to 26.9) vs. 26.2 (23.5 to 30.3) kg/m2, P<0.003]. The overall prevalence of self-reported OA was significantly higher with C282Y homozygosity than compound heterozygosity (53.4% vs. 32.5%; adjusted odds ratio [aOR] 2.4 [95% confidence interval 1.2-5.0]), as was self-reported OP (25.6% vs. 7.5%; aOR 3.5 [1.1-12.1]). Conclusion Patients with C282Y homozygosity may be at increased risk of musculoskeletal complications of HH.
- OrthoMortPred: Predicting one-year mortality following orthopedic hospitalization.Publication . Pires de Carvalho, Filipe Ricardo; Gavaia, Paulo; Brito Camacho, AntónioPredicting mortality risk following orthopedic surgery is crucial for informed decision-making and patient care. This study aims to develop and validate a machine learning model for predicting one-year mortality risk after orthopedic hospitalization and to create a personalized risk prediction tool for clinical use. We analyzed data from 3,132 patients who underwent orthopedic procedures at the Central Lisbon University Hospital Center from 2021 to 2023. Using the LightGBM algorithm, we developed a predictive model incorporating various clinical and administrative variables. We employed SHAP (SHapley Additive exPlanations) values for model interpretation and created a personalized risk prediction tool for individual patient assessment. Our model achieved an accuracy of 93% and an area under the ROC curve of 0.93 for predicting one-year mortality. Notably, 'EMERGENCY ADMISSION DATE TIME' emerged as the most influential predictor, followed by age and pre-operative days. The model demonstrated robust performance across different patient subgroups and outperformed traditional statistical methods. The personalized risk prediction tool provides clinicians with real-time, patient-specific risk assessments and insights into contributing factors. Our study presents a highly accurate model for predicting one-year mortality following orthopedic hospitalization. The significance of 'EMERGENCY ADMISSION DATE TIME' as the primary predictor highlights the importance of admission timing in patient outcomes. The accompanying personalized risk prediction tool offers a practical means of implementing this model in clinical settings, potentially improving risk stratification and patient care in orthopedic practice.
- OrthoMortPred: predicting one-year mortality following orthopedic hospitalizationPublication . Pires de Carvalho, Filipe Ricardo; Gavaia, Paulo; Brito Camacho, AntónioObjective: Predicting mortality risk following orthopedic surgery is crucial for informed decision-making and patient care. This study aims to develop and validate a machine learning model for predicting one-year mortality risk after orthopedic hospitalization and to create a personalized risk prediction tool for clinical use. Methods: We analyzed data from 3,132 patients who underwent orthopedic procedures at the Central Lisbon University Hospital Center from 2021 to 2023. Using the LightGBM algorithm, we developed a predictive model incorporating various clinical and administrative variables. We employed SHAP (SHapley Additive exPlanations) values for model interpretation and created a personalized risk prediction tool for individual patient assessment. Results: Our model achieved an accuracy of 93% and an area under the ROC curve of 0.93 for predicting one-year mortality. Notably, ’EMERGENCY ADMISSION DATE TIME’ emerged as the most influential predictor, followed by age and pre-operative days. The model demonstrated robust performance across different patient subgroups and outperformed traditional statistical methods. The personalized risk prediction tool provides clinicians with real-time, patient-specific risk assessments and insights into contributing factors. Conclusion: Our study presents a highly accurate model for predicting one-year mortality following orthopedic hospitalization. The significance of ’EMERGENCY ADMISSION DATE TIME’ as the primary predictor highlights the importance of admission timing in patient outcomes. The accompanying personalized risk prediction tool offers a practical means of implementing this model in clinical settings, potentially improving risk stratification and patient care in orthopedic practice.