A Prospective Observational Cohort of Clinical Outcomes in Medical Inpatients prescribed Pharmacological Thromboprophylaxis Using Different Clinical Risk Assessment Models(COMPT RAMs)

Ability of Caprini and Padua risk-assessment models to predict venous thromboembolism in a nationwide Veterans Affairs study

OBJECTIVE

Venous thromboembolism (VTE) is a preventable complication of hospitalization. Risk-stratification is the cornerstone of prevention. The Caprini and Padua are two of the most commonly used risk-assessment models (RAMs) to quantify VTE risk. Both models perform well in select, high-risk cohorts. Although VTE RAMs were designed for use in all hospital admissions, they are mostly tested in select, high-risk cohorts. We aim to evaluate the two RAMs in a large, unselected cohort of patients.

METHODS

We analyzed consecutive first hospital admissions of 1,252,460 unique surgical and non-surgical patients to 1298 Veterans Affairs facilities nationwide between January 2016 and December 2021. Caprini and Padua scores were generated using the Veterans Affairs' national data repository. We determined the ability of the two RAMs to predict VTE within 90 days of admission. In secondary analyses, we evaluated prediction at 30 and 60 days, in surgical vs non-surgical patients, after excluding patients with upper extremity deep vein thrombosis, in patients hospitalized ≥72 hours, after including all-cause mortality in a composite outcome, and after accounting for prophylaxis in the predictive model. We used area under the receiver operating characteristic curves (AUCs) as the metric of prediction.

RESULTS

A total of 330,388 (26.4%) surgical and 922,072 (73.6%) non-surgical consecutively hospitalized patients (total N = 1,252,460) were analyzed. Caprini scores ranged from 0 to 28 (median, 4; interquartile range [IQR], 3-6); Padua scores ranged from 0-13 (median, 1; IQR, 1-3). The RAMs showed good calibration and higher scores were associated with higher VTE rates. VTE developed in 35,557 patients (2.8%) within 90 days of admission. The ability of both models to predict 90-day VTE was low (AUCs: Caprini, 0.56; 95% confidence interval [CI], 0.56-0.56; Padua, 0.59; 95% CI, 0.58-0.59). Prediction remained low for surgical (Caprini, 0.54; 95% CI, 0.53-0.54; Padua, 0.56; 95% CI, 0.56-0.57) and non-surgical patients (Caprini, 0.59; 95% CI, 0.58-0.59; Padua, 0.59; 95% CI, 0.59-0.60). There was no clinically meaningful change in predictive performance in any of the sensitivity analyses.

CONCLUSIONS

Caprini and Padua RAM scores have low ability to predict VTE events in a cohort of unselected consecutive hospitalizations. Improved VTE RAMs must be developed before they can be applied to a general hospital population.

The Benefits and Imperative of Venous Thromboembolism Risk Screening for Hospitalized Patients: A Systematic Review

Venous thromboembolism (VTE) is a major preventable condition in hospitalized patients globally. This systematic review evaluates the effectiveness and clinical significance of venous thromboembolism (VTE) risk-screening protocols in preventing VTE events among hospitalized patients. Databases, including PubMed, Embase and Cochrane, were searched without date limits for studies comparing outcomes between hospitalized patients who did and did not receive VTE risk screening using standard tools. Twelve studies, enrolling over 139,420 patients, were included. Study quality was assessed using the ROBVIS tool. The results were summarized narratively. The findings show significant benefits of using VTE risk screening versus usual care across various outcomes. Using recommended tools, like Caprini, Padua and IMPROVE, allowed for the accurate identification of high-risk patients who benefited most from prevention. Formal screening was linked to much lower VTE rates, shorter hospital stays, fewer deaths and better use of preventive strategies matched to estimated clot risk. This review calls for the widespread adoption of VTE risk screening as an important safety step for at-risk hospital patients. More high-quality comparative research is needed to validate screening tools in different settings and populations. In summary, VTE risk screening is essential for healthcare systems to reduce life-threatening VTE events and improve patient outcomes through properly targeted preventive methods.

The Caprini Risk Score for Early Prediction of Mortality in Patients With Acute Coronary Syndrome

BACKGROUND

The Caprini Risk Score (CRS) is a validated predictive instrument for venous thrombosis. Previous investigators have shown that a high CRS is associated with a higher risk of mortality from thrombotic diseases.

OBJECTIVE

The aim of this study was to assess the association between the CRS and prognosis of patients with acute coronary syndrome (ACS).

METHODS

Secondary analysis of data from a retrospective cohort study was conducted. Patients were classified into 3 CRS-based categories (CRS ≤ 2, CRS = 3-4, and CRS ≥ 5, indicating low, medium, and high, respectively). Kaplan-Meier curves and Cox regression models were used to assess the prognosis of patients with ACS. All-cause mortality and cardiac mortality were the end points.

RESULTS

Two hundred fifty-four patients (12.8%) died during follow-up. Multivariate Cox regression models identified CRS as an independent risk factor for all-cause mortality among patients with ACS (CRS = 3-4 vs CRS ≤ 2, hazard ratio: 3.268, 95% confidence interval: 1.396-7.647, P = .006; CRS ≥ 5 vs CRS ≤ 2, hazard ratio: 4.099, 95% confidence interval: 1.708-9.841, P = .002). Pearson correlation analysis showed a positive correlation between CRS and fibrinogen level ( r = 0.486, R2 = 0.765, P < .001) as well as D-dimer level ( r = 0.480, R2 = 0.465, P < .001).

CONCLUSION

The CRS is a useful prognostic assessment instrument for patients with ACS, and the risk stratification of patients with ACS can be achieved based on their CRS at admission.

Ability of Caprini and Padua Risk-Assessment Models to Predict Venous Thromboembolism in a Nationwide Study

Background

Venous thromboembolism (VTE) is a preventable complication of hospitalization. Risk-stratification is the cornerstone of prevention. The Caprini and Padua are the most commonly used risk-assessment models to quantify VTE risk. Both models perform well in select, high-risk cohorts. While VTE risk-stratification is recommended for all hospital admissions, few studies have evaluated the models in a large, unselected cohort of patients.

Methods

We analyzed consecutive first hospital admissions of 1,252,460 unique surgical and non-surgical patients to 1,298 VA facilities nationwide between January 2016 and December 2021. Caprini and Padua scores were generated using the VA's national data repository. We first assessed the ability of the two RAMs to predict VTE within 90 days of admission. In secondary analyses, we evaluated prediction at 30 and 60 days, in surgical versus non-surgical patients, after excluding patients with upper extremity DVT, in patients hospitalized ≥72 hours, after including all-cause mortality in the composite outcome, and after accounting for prophylaxis in the predictive model. We used area under the receiver-operating characteristic curves (AUC) as the metric of prediction.

Results

A total of 330,388 (26.4%) surgical and 922,072 (73.6%) non-surgical consecutively hospitalized patients (total n=1,252,460) were analyzed. Caprini scores ranged from 0-28 (median, interquartile range: 4, 3-6); Padua scores ranged from 0-13 (1, 1-3). The RAMs showed good calibration and higher scores were associated with higher VTE rates. VTE developed in 35,557 patients (2.8%) within 90 days of admission. The ability of both models to predict 90-day VTE was low (AUCs: Caprini 0.56 [95% CI 0.56-0.56], Padua 0.59 [0.58-0.59]). Prediction remained low for surgical (Caprini 0.54 [0.53-0.54], Padua 0.56 [0.56-0.57]) and non-surgical patients (Caprini 0.59 [0.58-0.59], Padua 0.59 [0.59-0.60]). There was no clinically meaningful change in predictive performance in patients admitted for ≥72 hours, after excluding upper extremity DVT from the outcome, after including all-cause mortality in the outcome, or after accounting for ongoing VTE prophylaxis.

Conclusions

Caprini and Padua risk-assessment model scores have low ability to predict VTE events in a cohort of unselected consecutive hospitalizations. Improved VTE risk-assessment models must be developed before they can be applied to a general hospital population.

Prediction of patient choice tendency in medical decision-making based on machine learning algorithm

Objective

Machine learning (ML) algorithms, as an early branch of artificial intelligence technology, can effectively simulate human behavior by training on data from the training set. Machine learning algorithms were used in this study to predict patient choice tendencies in medical decision-making. Its goal was to help physicians understand patient preferences and to serve as a resource for the development of decision-making schemes in clinical treatment. As a result, physicians and patients can have better conversations at lower expenses, leading to better medical decisions.

Method

Patient medical decision-making tendencies were predicted by primary survey data obtained from 248 participants at third-level grade-A hospitals in China. Specifically, 12 predictor variables were set according to the literature review, and four types of outcome variables were set based on the optimization principle of clinical diagnosis and treatment. That is, the patient's medical decision-making tendency, which is classified as treatment effect, treatment cost, treatment side effect, and treatment experience. In conjunction with the study's data characteristics, three ML classification algorithms, decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), were used to predict patients' medical decision-making tendency, and the performance of the three types of algorithms was compared.

Results

The accuracy of the DT algorithm for predicting patients' choice tendency in medical decision making is 80% for treatment effect, 60% for treatment cost, 56% for treatment side effects, and 60% for treatment experience, followed by the KNN algorithm at 78%, 66%, 74%, 84%, and the SVM algorithm at 82%, 76%, 80%, 94%. At the same time, the comprehensive evaluation index F1-score of the DT algorithm are 0.80, 0.61, 0.58, 0.60, the KNN algorithm are 0.75, 0.65, 0.71, 0.84, and the SVM algorithm are 0.81, 0.74, 0.73, 0.94.

Conclusion

Among the three ML classification algorithms, SVM has the highest accuracy and the best performance. Therefore, the prediction results have certain reference values and guiding significance for physicians to formulate clinical treatment plans. The research results are helpful to promote the development and application of a patient-centered medical decision assistance system, to resolve the conflict of interests between physicians and patients and assist them to realize scientific decision-making.

Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed.

Systematic review of venous thromboembolism risk categories derived from Caprini score

OBJECTIVE

Hospital-acquired venous thromboembolism (VTE, including pulmonary embolism [PE] and deep vein thrombosis [DVT]) is a preventable cause of hospital death. The Caprini risk assessment model (RAM) is one of the most commonly used tools to assess VTE risk. The RAM is operationalized in clinical practice by grouping several risk scores into VTE risk categories that drive decisions on prophylaxis. A correlation between increasing Caprini scores and rising VTE risk is well-established. We assessed whether the increasing VTE risk categories assigned on the basis of recommended score ranges also correlate with increasing VTE risk.

METHODS

We conducted a systematic review of articles that used the Caprini RAM to assign VTE risk categories and that reported corresponding VTE rates. A Medline and EMBASE search retrieved 895 articles, of which 57 fulfilled inclusion criteria.

RESULTS

Forty-eight (84%) of the articles were cohort studies, 7 (12%) were case-control studies, and 2 (4%) were cross-sectional studies. The populations varied from postsurgical to medical patients. There was variability in the number of VTE risk categories assigned by individual studies (6 used 5 risk categories, 37 used 4, 11 used 3, and 3 used 2), and in the cutoff scores defining the risk categories (scores from 0 alone to 0-10 for the low-risk category; from ≥5 to ≥10 for high risk). The VTE rates reported for similar risk categories also varied across studies (0%-12.3% in the low-risk category; 0%-40% for high risk). The Caprini RAM is designed to assess composite VTE risk; however, two studies reported PE or DVT rates alone, and many of the other studies did not specify the types of DVTs analyzed. The Caprini RAM predicts VTE at 30 days after assessment; however, only 17 studies measured outcomes at 30 days; the remaining studies had either shorter or longer follow-ups (0-180 days).

CONCLUSIONS

The usefulness of the Caprini RAM is limited by heterogeneity in its implementation across centers. The score-derived VTE risk categorization has significant variability in the number of risk categories being used, the cutpoints used to define the risk categories, the outcome being measured, and the follow-up duration. This factor leads to similar risk categories being associated with different VTE rates, which impacts the clinical and research implications of the results. To enhance generalizability, there is a need for studies that validate the RAM in a broad population of medical and surgical patients, identify standardized risk categories, define risk of DVT and PE as distinct end points, and measure outcomes at standardized follow-up time points.