Clinical Decision-making Process Research Articles

Establishment of a nomogram for potential prediction of lung metastasis in patients with primary limb bone tumors: a study based on the SEER database.

The prognosis of lung metastasis in primary limb bone tumors represents a pivotal yet challenging aspect of oncological management. Despite advancements in diagnostic modalities, the predictive accuracy for metastatic spread remains suboptimal. This study aims to bridge this gap by leveraging the Surveillance, Epidemiology, and End Results (SEER) database to construct a nomogram that forecasts the risk of lung metastasis, thereby enhancing clinical decision-making processes. A retrospective cohort, including 1,822 patients with primary limb bony tumors from 2010 to 2015 in the SEER database, was extracted. Using precise inclusion and exclusion criteria, variables essential for predicting lung metastasis were identified through univariate and multivariate analyses, along with least absolute shrinkage and selection operator (LASSO) regression. These variables provided a solid basis for creating the multivariable nomogram, of which the discriminating power and utility were verified using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis. The model incorporated seven key predicting variables, including age, histological type, surgery, radiation, chemotherapy, T stage, and N stage. The nomogram emerged as a cohesive whole with good discriminative power. The area under the curve (AUC) was 0.806 in the training cohort and 0.767 in the validation cohort. The calibration curves demonstrated the model's validity by showing a good match between the actual outcomes and the model-predicted probabilities of lung metastasis. This study showed for the first time the reliability of the predictive model in translating the hard-to-interpret demographic, clinical, and pathologic data into a very usable predictive model. Thus, it represents a significant step toward demystifying the risk of lung metastasis in primary limb bone tumors. It is an invitation for a paradigm shift of oncology, to evidence-based, person-based oncology that is taking a new metric for cancer prognosis.

The role of cholesterol-modified prognostic nutritional index in nutritional status assessment and predicting survival after liver resection for hepatocellular carcinoma.

Malnutrition, which is often underestimated in patients with hepatocellular carcinoma (HCC), has a proven adverse effect on survival rates. The purpose of this study was to verify the effectiveness of the cholesterol-modified prognostic nutritional index (CPNI) in determining the nutritional status and predicting overall survival (OS) and recurrence-free survival (RFS) in patients with HCC by comparing it with several other nutritional indicators. This retrospective single-center study enrolled 1450 consecutive HCC patients who underwent curative liver resection from January 2015 to November 2019. We evaluated the prognostic significance of several nutritional indicators, including CPNI, the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI), by applying time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier survival analysis, and Cox proportional hazards regression analysis. Among several objective nutrition evaluations (including CPNI, CONUT, NRI, and PNI), CPNI demonstrated the greatest prognostic predictive power for predicting OS. Meanwhile, CPNI demonstrated marginally higher accuracy in predicting RFS compared to PNI, and significantly outperformed CONUT and NRI. Univariate and multivariate analyses suggested that CPNI was an independent risk factor for the OS and RFS of patients with HCC undergoing curative liver resection. In most subgroups, malnutrition as identified by CPNI demonstrates strong stratification ability in predicting both OS and RFS. CPNI serves as an accurate and stable instrument for evaluating nutritional status and forecasting survival outcomes in HCC patients following liver resection, which has the potential to markedly influence clinical decision-making processes and the management of patient care.

Variation in Antibiotic Prescription in High-Risk Febrile Neutropenia in Portuguese Hospitals.

Febrile neutropenia (FN) is a potentially severe entity, particularly in hemato-oncologic patients who have higher incidence of colonization with multidrug-resistant bacteria. Discrepancies among guidelines contribute to divergence in antimicrobial practices. Our objective was to assess the variation of practices in antimicrobial therapy in high-risk FN among Portuguese hematologists. We conducted a cross-sectional study through the implementation of an online survey, open to all clinical hematologists in the country. To characterize practice patterns regarding critical elements in FN management, three clinical vignettes were designed to describe typical situations where narrow-spectrum empiric antibiotics (vignette 1), short-course therapy (vignette 2) and de-escalation (vignette 3) could be performed. The remaining questions characterized clinical experience, department size, and differentiation and decision-making process regarding FN antibiotic therapy. The survey yielded 31 responses from 11 hospitals across four regions. All respondents opted for empiric narrow-spectrum antibiotics, 22.6% opted for short-course therapy (mostly senior specialists from larger settings) and 35.5% for de-escalation (mostly young specialists). Availability of an FN protocol seemed to favor both approaches. These findings should be complemented by qualitative assessments of barriers to best practices and should support the need for interventions to improve antibiotic use in febrile neutropenia.

Constructing a Nomogram for Predicting Pelvic Osteosarcoma: A Retrospective Study Based on the SEER Database and a Chinese Cohort.

Aims/Background Generally, pelvic osteosarcoma has a worse prognosis compared with limb osteosarcoma. This study aims to create and validate a new nomogram for predicting the prognosis of pelvic osteosarcoma. Methods Clinical data of 62 patients derived from the Surveillance, Epidemiology, and End Results (SEER) database and 31 Chinese patients diagnosed with pelvic osteosarcoma were gathered. Kaplan-Meier survival analysis was utilized to calculate the median survival time for all variables. Univariate and multivariate Cox regression models were employed to identify the prognostic factors of pelvic osteosarcoma. A nomogram was constructed using data gleaned from the SEER cohort and verified using the receiver operating characteristic (ROC) curve and calibration plot in the Chinese cohort. Results Kaplan-Meier analysis revealed that individuals of other races (Asians) (hazard ratio (HR) = 0.24, 95% confidence interval (CI): 0.1-0.57, p = 0.001), aged ≤51 years old (HR = 0.4, 95% CI: 0.22-0.73, p = 0.003), and with tumor size ≤160 mm (HR = 0.37, 95% CI: 0.2-0.71, p = 0.03) had better survival outcomes. Conversely, factors such as no primary surgery (HR = 3.6, 95% CI: 1.81-7.15, p < 0.001), lung metastasis (HR = 1.96, 95% CI: 1.17-3.28, p = 0.010), and radiotherapy (HR = 1.89, 95% CI: 1.10-3.25, p = 0.021) were associated with poorer survival. Multivariate Cox analysis indicated that lung metastasis (HR = 2.57, 95% CI: 1.29-5.13, p = 0.008), other races (Asians) (HR = 0.23, 95% CI: 0.07-0.75, p = 0.015), tumor size (HR = 0.28, 95% CI: 0.13-0.62, p = 0.001) and age (HR = 0.3, 95% CI: 0.16-0.59, p < 0.001) were independent prognostic factors for pelvic osteosarcoma. Univariate and multivariate Cox regression models identified three independent variables in the training cohort: age, lung metastasis, and tumor size. A predictive nomogram was developed based on the data from the SEER cohort and validated in the Chinese cohort. The areas under the curves (AUCs) that are used to predict 1-year, 2-year, and 3-year survival rates were 0.81 (95% CI: 0.68-0.94), 0.75 (95% CI: 0.63-0.86), and 0.80 (95% CI: 0.70-0.89) in the training cohort, and 0.67 (95% CI: 0.30-1.04), 0.66 (95% CI: 0.43-0.90) and 0.71 (95% CI: 0.50-0.93) in the validation cohort. Conclusion The predictive nomogram constructed in this study facilitates accurate and effective prediction of the overall survival of patients with pelvic osteosarcoma and helps enhance the clinical decision-making process.

Impact of Data Governance on Data Quality in Healthcare Institutions

Purpose: The aim of the study was to assess the impact of data governance on data quality in healthcare institutions. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that institutions with robust data governance protocols experience fewer instances of data inconsistency and errors. This, in turn, improves the reliability of clinical decision-making processes and patient outcomes. Moreover, compliant data governance practices contribute to better data integration across various healthcare systems and departments, facilitating comprehensive patient care and longitudinal health monitoring. By promoting transparency and accountability in data management, healthcare institutions can mitigate risks associated with data breaches and ensure compliance with regulatory standards, thereby fostering trust among patients and stakeholders alike. Implications to Theory, Practice and Policy: Control theory, information theory and agency theory may be used to anchor future studies on assessing the impact of data governance on data quality in healthcare institutions. Healthcare institutions should prioritize the adoption of comprehensive data governance frameworks that integrate policy development, stakeholder engagement, and technological infrastructure. Policymakers should align regulatory frameworks with evolving data governance standards to promote interoperability and data exchange across healthcare networks.

Biomolecular Classification in Endometrial Cancer: Onset, Evolution, and Further Perspectives: A Critical Review.

Since the new guidelines for endometrial cancer risk classification have been published, many reviews have proposed a critical re-evaluation. In this review, we look back to how the molecular classification system was built and its evolution in time to highlight the major flaws, particularly the biases stemming from the inherent limitations of the cohorts involved in the discoveries. A significant drawback in some cohorts is the inclusion criteria, as well as the retrospective nature and the notably sparse numbers, especially in the POLEmut (nonsynonymous mutation in EDM domain of POLE) risk groups, all of which impact the reliability of outcomes. Additionally, a disregard for variations in follow-up duration leads to a non-negligible bias, which raises a substantial concern in data interpretation and guideline applicability. Finally, according to the results that we obtained through a re-analysis of the confirmation cohort, the p53abn (IHC positive for p53 protein) subgroup, which is predominant in non-endometrioid histology (73-80%), loses its predictivity power in the endometrioid cohort of patients. The exclusion of non-endometrioid subtypes from the cohort led to a complete overlap of three molecular subgroups (all except POLEmut) for both overall and progression-free survival outcomes, suggesting the need for a more histotype-specific approach. In conclusion, this review challenges the current ESGO/ESTRO/ESP guidelines on endometrial cancer risk classification and highlights the limitations that must be addressed to better guide the clinical decision-making process.

A delayed diagnosis of hyperthyroidism in a patient with persistent vomiting in the presence of Chiari type 1 malformation.

To present and discuss an uncommon clinical presentation of hyperthyroidism in a female patient with Chiari type 1 malformation. We explore how her medical history influenced the diagnostic process and ultimately contributed to the delayed diagnosis. In this case study, we discuss an unusual presentation of hyperthyroidism in a 35-year-old female with Chiari type 1 malformation. Initially experiencing headaches, tremors, and dizziness, the patient consulted multiple specialists without a clear diagnosis. Later, she developed recurrent vomiting unrelated to food intake, significant weight loss (12 kg), and muscle weakness, leading to her hospitalization. After six months of clinical evaluation with several specialists (neurologists, neurosurgeons, and gastroenterologists), she was,finally, diagnosed with hyperthyroidism by an Internal Medicine physician in another private clinic. Treatment with thiamazole and propranolol led to the improvement of symptoms progressively. This caseemphasizes the vital role of clinical reasoning, crucial problem-solving, and decision-making processes while addressing cognitive biases in medical specialization. Besides, it highlights the need for internist evaluation in outpatient care to ensure comprehensive assessment and prompt specialist referrals if needed. This case accentuates the importance of internist evaluation for comprehensive care and timely specialist referrals. Recognizing unusual presentations, like thyrotoxic vomiting, and addressing cognitive biases, such as confirmation and anchor biases, are crucial for accurate and prompt diagnosis. This approach enhances diagnostic accuracy, minimizing unnecessary tests and costs, and alleviates patient suffering.

Explainable Machine Learning Approach to Prediction of Prolonged Intesive Care Unit Stay in Adult Spinal Deformity Patients: Machine Learning Outperforms Logistic Regression.

Retrospective cohort study. Prolonged ICU stay is a driver of higher costs and inferior outcomes in Adult Spinal Deformity (ASD) patients. Machine learning (ML) models have recently been seen as a viable method of predicting pre-operative risk but are often 'black boxes' that do not fully explain the decision-making process. This study aims to demonstrate ML can achieve similar or greater predictive power as traditional statistical methods and follows traditional clinical decision-making processes. Five ML models (Decision Tree, Random Forest, Support Vector Classifier, GradBoost, and a CNN) were trained on data collected from a large urban academic center to predict whether prolonged ICU stay would be required post-operatively. 535 patients who underwent posterior fusion or combined fusion for treatment of ASD were included in each model with a 70-20-10 train-test-validation split. Further analysis was performed using Shapley Additive Explanation (SHAP) values to provide insight into each model's decision-making process. The model's Area Under the Receiver Operating Curve (AUROC) ranged from 0.67 to 0.83. The Random Forest model achieved the highest score. The model considered length of surgery, complications, and estimated blood loss to be the greatest predictors of prolonged ICU stay based on SHAP values. We developed a ML model that was able to predict whether prolonged ICU stay was required in ASD patients. Further SHAP analysis demonstrated our model aligned with traditional clinical thinking. Thus, ML models have strong potential to assist with risk stratification and more effective and cost-efficient care.

Advancing thyroid care: An accurate trustworthy diagnostics system with interpretable AI and hybrid machine learning techniques

The worldwide prevalence of thyroid disease is on the rise, representing a chronic condition that significantly impacts global mortality rates. Machine learning (ML) approaches have demonstrated potential superiority in mitigating the occurrence of this disease by facilitating early detection and treatment. However, there is a growing demand among stakeholders and patients for reliable and credible explanations of the generated predictions in sensitive medical domains. Hence, we propose an interpretable thyroid classification model to illustrate outcome explanations and investigate the contribution of predictive features by utilizing explainable AI. Two real-time thyroid datasets underwent various preprocessing approaches, addressing data imbalance issues using the Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTE-ENN). Subsequently, two hybrid classifiers, namely RDKVT and RDKST, were introduced to train the processed and selected features from Univariate and Information Gain feature selection techniques. Following the training phase, the Shapley Additive Explanation (SHAP) was applied to identify the influential characteristics and corresponding values contributing to the outcomes. The conducted experiments ultimately concluded that the presented RDKST classifier achieved the highest performance, demonstrating an accuracy of 98.98 % when trained on Information Gain selected features. Notably, the features T3 (triiodothyronine), TT4 (total thyroxine), TSH (thyroid-stimulating hormone), FTI (free thyroxine index), and T3_measured significantly influenced the generated outcomes. By balancing classification accuracy and outcome explanation ability, this study aims to enhance the clinical decision-making process and improve patient care.

Management of Peri-implant Mucosal Dehiscences: A Scoping Review.

Several treatment-oriented classifications for the management of peri-implant marginal mucosal defects (PMMDs) have been published to date. While each classification provides valuable insights into key diagnostic and therapeutic aspects, there is a marked heterogeneity regarding the recommended clinical guidelines to achieve success in specific scenarios. The purpose of this review was to critically analyze and organize the similarities and differences enclosed in the available classifications linked with treatment recommendations on the management of PMMDs at single implant non-molar sites with the purpose of providing an overview of recommended interdisciplinary treatment options to facilitate clinical decision-making processes.

Personalized approach to malignant struma ovarii: Insights from a web-based machine learning tool.

Malignant struma ovarii (MSO) is a rare ovarian tumor characterized by mature thyroid tissue. The diverse symptoms and uncommon nature of MSO can create difficulties in its diagnosis and treatment. This study aimed to analyze data and use machine learning methods to understand the prognostic factors and potential management strategies for MSO. In this retrospective cohort, the Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five machine learning algorithms to predict the 5-year survival. A validation method incorporating the area under the curve of the receiver operating characteristic curve was used to validate the accuracy and reliability of the machine learning models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. The study population comprised 329 patients. Multivariate Cox regression analysis revealed that older age, unmarried status, chemotherapy, and the total number of tumors in patients were poor prognostic factors. Machine learning models revealed that the multilayer perceptron accurately predicted outcomes, followed by the random forest classifier, gradient boosting classifier, K-nearest neighbors, and logistic regression models. The factors that contributed the most were age, marital status, and the total number of tumors in the patients. The present study offers a comprehensive approach for the treatment and prognosis assessment of patients with MSO. The machine learning models we have developed serve as a practical, personalized tool to aid in clinical decision-making processes.

Determinants of adverse outcomes following patent foramen ovale closure in elderly patients.

Limited data are available on transcatheter patent foramen ovale (PFO) closure outcomes in the elderly. Through this study, we aimed to determine the incidence and predictors of adverse events (recurrent cerebrovascular events [CVE] and atrial fibrillation [AF]) post-PFO closure in older patients with cryptogenic events. This multicentre international study included patients over 60 years undergoing PFO closure for cryptogenic thromboembolic events. A dedicated database compiled baseline, procedural, and follow-up data. Competing risk and adjusted outcome predictor analyses were conducted. A total of 689 patients were included (median age 65 years, 41.2% female, mean Risk of Paradoxical Embolism [RoPE] score 4.5). The procedural success rate was 99.4%. After a median follow-up of 2 (interquartile range 1-5) years, 66 patients (9.6%) had died. CVE and stroke rates were 1.21 and 0.55 per 100 patient-years, respectively. Diabetes (hazard ratio [HR] 3.89, 95% confidence interval [CI]: 1.67-9.07; p=0.002) and atrial septal aneurysm (ASA; HR 5.25, 95% CI: 1.56-17.62; p=0.007) increased the CVE risk. New-onset AF occurred at a rate of 3.30 per 100 patient-years, with 51.3% within one month post-procedure. Older age (HR 1.05 per year, 95% CI: 1.00-1.09; p=0.023) and the absence of hypertension (HR 2.04, 95% CI: 1.19-3.57; p=0.010) were associated with an increased risk of AF. Older patients undergoing PFO closure had a relatively low rate of CVE and new-onset AF after a median follow-up of 2 years. The presence of diabetes, ASA, and a more advanced age determined an increased risk of adverse clinical events. These factors may be considered in the clinical decision-making process regarding PFO closure in this challenging population.

Patient-centered clinical decision support challenges and opportunities identified from workflow execution models.

To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows. To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee. We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new or modified policies, processes, procedures, technology, and expertise to: (1) Ensure patient-generated health data (PGHD), including patient-reported outcomes (PROs), are documented, reviewed, and managed by appropriately trained clinicians, between visits and after regular working hours. (2) Educate patients to use connected medical devices and handle technical issues. (3) Facilitate collection and incorporation of PGHD, PROs, patient preferences, and social determinants of health into existing electronic health records. (4) Troubleshoot erroneous data received from devices. (5) Develop dashboards to display longitudinal patient-reported data. (6) Provide reimbursement to support new models of care. (7) Support patient engagement with remote devices. Several new policies, processes, technologies, and expertise are required to ensure safe and effective implementation and use of PC CDS. As we gain more experience implementing and working with PC CDS, we should be able to begin realizing the long-term positive impact on patient health that the patient-centered movement in healthcare promises.

Current Role of Cardiac Magnetic Resonance for the Clinical Decision-Making Process in Transcatheter Aortic Valve Replacement

Current Role of Cardiac Magnetic Resonance for the Clinical Decision-Making Process in Transcatheter Aortic Valve Replacement

Assessment of universities that provide education in the field of health tourism in Türkiye in the light of current data

Introduction: Health tourism, defined as travels aimed to improve the physical and mental health of individuals, is growing day by day in parallel with developing technology and transportation. Factors influencing the preference of Türkiye in health tourism include cost, tourism opportunities, and cultural characteristics, as well as health facilities, staff, doctors, and the quality level of service. Due to the focus of human health, the service delivery which is very sensitive should be carried out by qualified workforce. With their role in protecting patients from unnecessary tests and treatments and facilitating effective participation in clinical decision-making processes, family physicians are also involved in the health tourism process and advocate. It is aimed to improve the service provision both qualitatively and quantitatively to meet the predicted increase in the health tourism sector in our country. In line with the realization of these goals, education and training activities offered by higher education institutions come into prominence. It’s aimed in our study to quantitatively investigate the adequacy of education in the field of health tourism in our country and to assess it in light of current data. Methods: The data of the cross-sectional and descriptive study were collected during the 2023-2024 academic years. Education programs and the number of students in the field of health tourism between 2018-2023 at 18 institutions under the Council of Higher Education (YOK) and The Measuring, Selection and Placement Center (OSYM) were examined. Obtained data were brought together with health tourism data published by the Turkish Statistical Institute (TUIK), the International Health Services Inc. (USHAS), and the Association of Turkish Travel Agencies (TURSAB). Results: In Türkiye, for the first time in 2014, associate degree education started to be provided in the " Health Tourism Business" department within the Vocational School. This department, which is available in 9 universities today, has the highest percentage among associate degree preferences. Master's degree program level trainings in health tourism continue in 9 universities across 3 different departments: "Health Tourism", " Health Tourism Business " and "Health Tourism Management". Doctoral level education is provided in 1 university in the department of "Health Tourism". It is observed that the number of students and graduates enrolled in education programs in the field of health tourism is increasing every year. On the other hand, there is still no undergraduate program in this field in Türkiye. According to the data published by TUIK and USHAS, the number of visitors coming to our country for health tourism and the value of income is increasing every year. On the contrary, the number of students and graduates has not increased at the same rate in recent years. Conclusions: The number of programs and quotas in the field of health tourism opened in universities is gradually increasing. However, when evaluated together with the increase in the number of visitors coming to our country for health tourism over the years, it has been revealed that the number of qualified staff trained to meet the increasing need is still not at the desired level.

Machine learning algorithms predict canine structural epilepsy with high accuracy.

Clinical reasoning in veterinary medicine is often based on clinicians' personal experience in combination with information derived from publications describing cohorts of patients. Studies on the use of scientific methods for patient individual decision making are largely lacking. This applies to the prediction of the individual underlying pathology in seizuring dogs as well. The aim of this study was to apply machine learning to the prediction of the risk of structural epilepsy in dogs with seizures. Dogs with a history of seizures were retrospectively as well as prospectively included. Data about clinical history, neurological examination, diagnostic tests performed as well as the final diagnosis were collected. For data analysis, the Bayesian Network and Random Forest algorithms were used. A total of 33 features for Random Forest and 17 for Bayesian Network were available for analysis. The following four feature selection methods were applied to select features for further analysis: Permutation Importance, Forward Selection, Random Selection and Expert Opinion. The two algorithms Bayesian Network and Random Forest were trained to predict structural epilepsy using the selected features. A total of 328 dogs of 119 different breeds were identified retrospectively between January 2017 and June 2021, of which 33.2% were diagnosed with structural epilepsy. An overall of 89,848 models were trained. The Bayesian Network in combination with the Random feature selection performed best. It was able to predict structural epilepsy with an accuracy of 0.969 (sensitivity: 0.857, specificity: 1.000) among all dogs with seizures using the following features: age at first seizure, cluster seizures, seizure in last 24 h, seizure in last 6 month, and seizure in last year. Machine learning algorithms such as Bayesian Networks and Random Forests identify dogs with structural epilepsy with a high sensitivity and specificity. This information could provide some guidance to clinicians and pet owners in their clinical decision-making process.

A robust ensemble deep learning framework for accurate diagnoses of tuberculosis from chest radiographs.

Tuberculosis (TB) stands as a paramount global health concern, contributing significantly to worldwide mortality rates. Effective containment of TB requires deployment of cost-efficient screening method with limited resources. To enhance the precision of resource allocation in the global fight against TB, this research proposed chest X-ray radiography (CXR) based machine learning screening algorithms with optimization, benchmarking and tuning for the best TB subclassification tasks for clinical application. This investigation delves into the development and evaluation of a robust ensemble deep learning framework, comprising 43 distinct models, tailored for the identification of active TB cases and the categorization of their clinical subtypes. The proposed framework is essentially an ensemble model with multiple feature extractors and one of three fusion strategies-voting, attention-based, or concatenation methods-in the fusion stage before a final classification. The comprised de-identified dataset contains records of 915 active TB patients alongside 1,276 healthy controls with subtype-specific information. Thus, the realizations of our framework are capable for diagnosis with subclass identification. The subclass tags include: secondary tuberculosis/tuberculous pleurisy; non-cavity/cavity; secondary tuberculosis only/secondary tuberculosis and tuberculous pleurisy; tuberculous pleurisy only/secondary tuberculosis and tuberculous pleurisy. Based on the dataset and model selection and tuning, ensemble models show their capability with self-correction capability of subclass identification with rendering robust clinical predictions. The best double-CNN-extractor model with concatenation/attention fusion strategies may potentially be the successful model for subclass tasks in real application. With visualization techniques, in-depth analysis of the ensemble model's performance across different fusion strategies are verified. The findings underscore the potential of such ensemble approaches in augmenting TB diagnostics with subclassification. Even with limited dataset, the self-correction within the ensemble models still guarantees the accuracies to some level for potential clinical decision-making processes in TB management. Ultimately, this study shows a direction for better TB screening in the future TB response strategy.

Exploring actigraphy as a digital phenotyping measure: A study on differentiating psychomotor agitation and retardation in depression.

Psychomotor activity stands out as a crucial symptom in characterizing behaviors associated with depression. This study aims to explore the potential of actigraphy as a tool for digital phenotyping in characterizing symptoms of psychomotor agitation and retardation, which are clinically challenging dimensions to capture, in patients diagnosed with major depressive episode (MDE) according to DSM-5 criteria. We compared rest-activity circadian rhythm biomarkers measured by the Motion Watch 8 actigraphy between 58 (78.4%) patients with MDE and psychomotor retardation (PMR), and 16 (21.6%) patients with MDE and psychomotor agitation (PMA), according to DSM-5 criteria. Actigraphy allowed to objectively report PMA through heightened activity over a 24-h period, while PMR manifests as reduced activity during the most active 10 h. Lower rest-activity rhythm (RAR) amplitude in PMR was accompanied by increased irregularities in intra- and inter-day rhythms. Interestingly, actigraphy emerges as an objective tool to measure the characteristics of the active and rest periods, free from the confounding effects of sleep disturbances. Indeed, no differences in sleep disturbances were identified between patients exhibiting psychomotor agitation and those displaying PMR. Digital phenotyping through actigraphy may aid in distinguishing psychomotor retardation and psychomotor agitation allowing for a more precise characterization of the depression phenotype. When integrated with clinical assessment, measurements from actigraphy could offer additional insights into activity rhythms alongside subjective assessments and hold the potential to augment existing clinical decision-making processes in psychiatry.

UNNECESSARY USE OF ANTIMICROBIALS – COMPARISON OF THE PREDICTIVE PERFORMANCE OF QSOFA AND APACHE SCORE FOR EVALUATION OF DISEASE PROGNOSIS PATTERNS IN CRITICAL ILLNESS

In critical care, accurate prognosis assessment, including antimicrobial therapy, is vital for guiding treatment decisions. The Acute Physiology and Chronic Health Evaluation (APACHE) and quick Sequential Organ Failure Assessment (qSOFA) scores are widely used for predicting patient outcomes. Yet, their relative performance and potential implications for antimicrobial stewardship need further exploration. Objective: To evaluate the disease prognosis patterns in critically ill patients by comparing the predictive performance of APACHE and qSOFA scores and to explore their correlation with antimicrobial misuse. Methods: A comparative study was conducted at the National Hospital and Medical Center in Lahore from January to November 2023. The study included 138 critically ill patients. Demographic information, clinical parameters, and medical history were collected, explicitly focusing on APACHE and qSOFA scores recorded at admission and during follow-up. Statistical analysis, including Pearson's and Spearman's correlation coefficients and logistic regression models, was performed using RStudio to assess the predictive ability of these scores for disease progression and patient outcomes. Results: The analysis revealed a moderate positive correlation (r=0.51) between qSOFA and APACHE-II scores, suggesting that both scores align in assessing the severity of illness in critically ill patients. The predictive models indicated that combining qSOFA and APACHE-II scores enhanced the accuracy of predicting critical outcomes, such as shortness of breath (SOB), with a combined model AUC of 0.659 compared to 0.601 for the APACHE-II model alone. Conclusion: The findings underscore the value of integrating multiple clinical scoring systems in managing critically ill patients. Such integration can aid in more judicious use of antibiotics, potentially mitigating the risk of antimicrobial resistance. The study advocates incorporating these insights into clinical guidelines and decision-making processes in critical care settings.

Advances in clinical repair techniques for localized knee cartilage lesions

To summarize the classic and latest treatment techniques for localized knee cartilage lesions in clinical practice and create a new comprehensive clinical decision-making process. The advantages and limitations of various treatment methods for localized knee cartilage lesions were summarized by extensive review of relevant literature at home and abroad in recent years. Currently, there are various surgical methods for treating localized knee cartilage injuries in clinical practice, each with its own pros and cons. For patients with cartilage injuries less than 2 cm 2 and 2-4 cm 2 with bone loss are recommended to undergo osteochondral autograft (OAT) and osteochondral allograft (OCA) surgeries. For patients with cartilage injuries less than 2 cm 2 and 2-4 cm 2 without bone loss had treatment options including bone marrow-based techniques (micro-fracture and ogous matrix induced chondrogenesis), autologous chondrocyte implantation (ACI)/matrix-induced ACI, particulated juvenile allograft cartilage (PJAC), OAT, and OCA. For patients with cartilage injuries larger than 4 cm 2 with bone loss were recommended to undergo OCA. For patients with cartilage injuries larger than 4 cm 2 without bone loss, treatment options included ACI/matrix-induced ACI, OAT, and PJAC. There are many treatment techniques available for localized knee cartilage lesions. Treatment strategy selection should be based on the size and location of the lesion, the extent of involvement of the subchondral bone, and the level of evidence supporting each technique in the literature.