Predicting the occurrence of venous thromboembolism: construction and verification of risk warning model

A composite risk assessment model for venous thromboembolism

OBJECTIVE

Venous thromboembolism (VTE) is a preventable cause of hospitalization-related morbidity and mortality. VTE prevention requires accurate risk stratification. Federal agencies mandated VTE risk assessment for all hospital admissions. We have shown that the widely used Caprini (30 risk factors) and Padua (11 risk factors) VTE risk-assessment models (RAMs) have limited predictive ability for VTE when used for all general hospital admissions. Here, we test whether combining the risk factors from all 23 available VTE RAMs improves VTE risk prediction.

METHODS

We analyzed data from the first hospitalizations of 1,282,014 surgical and non-surgical patients admitted to 1298 Veterans Affairs facilities nationwide between January 2016 and December 2021. We used logistic regression to predict VTE within 90 days of admission using risk factors from all 23 available VTE RAMs. Area under the receiver operating characteristic curves (AUC), sensitivity, specificity, and positive (PPV) and negative predictive values (NPV) were used to quantify the predictive power of our models. The metrics were computed at two diagnostic thresholds that maximized (1) the value of sensitivity + specificity-1; and (2) PPV and were compared using McNemar's test. The Delong-Delong test was used to compare AUCs.

RESULTS

After excluding those with missing data, 1,185,633 patients (mean age, 66 years; 93% male; and 72% White) were analyzed, of whom 33,253 (2.8%) had a VTE (deep venous thrombosis [DVT], n = 19,218, 1.6%; pulmonary embolism [PE], n = 10,190, 0.9%; PE + DVT, n = 3845, 0.3%). Our composite RAM included 102 risk factors and improved prediction of VTE compared with the Caprini RAM risk factors (AUC composite model: 0.74; AUC Caprini risk-factor model: 0.63; P < .0001). When the sum of sensitivity and specificity-1 was maximized, the composite model demonstrated small improvements in sensitivity, specificity and PPV; NPV was high in both models. When PPV was maximized, the PPV of the composite model was improved but remained low. The nature of the relationship between NPV and PPV precluded any further gain in PPV by sacrificing NPV and sensitivity.

CONCLUSIONS

Using a composite of 102 risk factors from all available VTE RAMs, we improved VTE prediction in a large, national cohort of >1 million general hospital admissions. However, neither model has a sensitivity or PPV that permits it to be a reliable predictor of VTE. We demonstrate the limits of currently available VTE risk prediction tools; no available RAM is ready for widespread use in the general hospital population.

Evaluation of venous thromboembolism risk assessment models for hospital inpatients: the VTEAM evidence synthesis

Background

Pharmacological prophylaxis during hospital admission can reduce the risk of acquired blood clots (venous thromboembolism) but may cause complications, such as bleeding. Using a risk assessment model to predict the risk of blood clots could facilitate selection of patients for prophylaxis and optimise the balance of benefits, risks and costs.

Objectives

We aimed to identify validated risk assessment models and estimate their prognostic accuracy, evaluate the cost-effectiveness of different strategies for selecting hospitalised patients for prophylaxis, assess the feasibility of using efficient research methods and estimate key parameters for future research.

Design

We undertook a systematic review, decision-analytic modelling and observational cohort study conducted in accordance with Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines.

Setting

NHS hospitals, with primary data collection at four sites.

Participants

Medical and surgical hospital inpatients, excluding paediatric, critical care and pregnancy-related admissions.

Interventions

Prophylaxis for all patients, none and according to selected risk assessment models.

Main outcome measures

Model accuracy for predicting blood clots, lifetime costs and quality-adjusted life-years associated with alternative strategies, accuracy of efficient methods for identifying key outcomes and proportion of inpatients recommended prophylaxis using different models.

Results

We identified 24 validated risk assessment models, but low-quality heterogeneous data suggested weak accuracy for prediction of blood clots and generally high risk of bias in all studies. Decision-analytic modelling showed that pharmacological prophylaxis for all eligible is generally more cost-effective than model-based strategies for both medical and surgical inpatients, when valuing a quality-adjusted life-year at £20,000. The findings were more sensitive to uncertainties in the surgical population; strategies using risk assessment models were more cost-effective if the model was assumed to have a very high sensitivity, or the long-term risks of post-thrombotic complications were lower. Efficient methods using routine data did not accurately identify blood clots or bleeding events and several pre-specified feasibility criteria were not met. Theoretical prophylaxis rates across an inpatient cohort based on existing risk assessment models ranged from 13% to 91%.

Limitations

Existing studies may underestimate the accuracy of risk assessment models, leading to underestimation of their cost-effectiveness. The cost-effectiveness findings do not apply to patients with an increased risk of bleeding. Mechanical thromboprophylaxis options were excluded from the modelling. Primary data collection was predominately retrospective, risking case ascertainment bias.

Conclusions

Thromboprophylaxis for all patients appears to be generally more cost-effective than using a risk assessment model, in hospitalised patients at low risk of bleeding. To be cost-effective, any risk assessment model would need to be highly sensitive. Current evidence on risk assessment models is at high risk of bias and our findings should be interpreted in this context. We were unable to demonstrate the feasibility of using efficient methods to accurately detect relevant outcomes for future research.

Future work

Further research should evaluate routine prophylaxis strategies for all eligible hospitalised patients. Models that could accurately identify individuals at very low risk of blood clots (who could discontinue prophylaxis) warrant further evaluation.

Study registration

This study is registered as PROSPERO CRD42020165778 and Researchregistry5216.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR127454) and will be published in full in Health Technology Assessment; Vol. 28, No. 20. See the NIHR Funding and Awards website for further award information.

A nomogram model for predicting lower extremity deep vein thrombosis after gynecologic laparoscopic surgery: a retrospective cohort study

Objective

To investigate the risk factors associated with lower extremity deep vein thrombosis (LEDVT) and to establish a predictive model for patients who undergo gynecologic laparoscopic surgery.

Methods

A review of clinical data was conducted on patients who underwent gynecologic laparoscopic surgery between November 1, 2020, and January 31, 2022. Patients who developed LEDVT after surgery were included as the observation group, while the control group comprised patients who did not experience complications. Multivariate forward stepwise logistic regression models were used to identify independent risk factors associated with LEDVT. A nomogram model was then developed based on these risk factors.

Results

A total of 659 patients underwent gynecologic laparoscopic surgery during the study period, and 52 (7.89%) of these patients developed postoperative LEDVT. Multivariate logistic regression analysis showed that older age (adjusted OR, 1.085; 95% CI [1.034-1.138]; P < 0.05), longer operation duration (adjusted OR, 1.014; 95% CI [1.009-1.020]; P < 0.05), shorter activated partial thromboplastin time (APTT) (adjusted OR, 0.749; 95% CI [0.635-0.884]; P < 0.05), higher D-dimer (adjusted OR, 4.929; 95% CI [2.369-10.255]; P < 0.05), higher Human Epididymis Protein 4 (HE4) (adjusted OR, 1.007; 95% CI [1.001-1.012]; P < 0.05), and history of hypertension (adjusted OR, 3.732; 95% CI [1.405-9.915]; P < 0.05) were all independent risk factors for LEDVT in patients who underwent gynecologic laparoscopic surgery. A nomogram model was then created, which had an area under the curve of 0.927 (95% CI [0.893-0.961]; P < 0.05), a sensitivity of 96.1%, and a specificity of 79.5%.

Conclusions

A nomogram model that incorporates information on age, operation duration, APTT, D-dimer, history of hypertension, and HE4 could effectively predict the risk of LEDVT in patients undergoing gynecologic laparoscopic surgery, potentially helping to prevent the development of this complication.

Associations between SARS-CoV-2 infections and thrombotic complications necessitating surgical intervention: A systematic review

BACKGROUND

Several unique clinical features of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19) infection, have been identified and characterized. One such feature, mostly among patients with severe COVID-19 infection, has become known as COVID-19-induced coagulopathy. Surgical patients with a history of or active COVID-19 infection bear a significantly higher risk for postoperative thrombotic complications. These patients may require surgical intervention to treat severe thrombotic complications. Few studies have been carried out to better characterize this association. The purpose of this study was to perform a systematic review and meta-analysis of the literature on COVID-19 infections that led to thrombotic complications necessitating surgical intervention. We hypothesized that patients with recent or active COVID-19 infection would have high rates of thromboembolic complications both arterial and venous in origin.

AIM

To perform a systematic review and meta-analysis of the literature on COVID-19 infections that led to thrombotic complications necessitating surgical intervention.

METHODS

The current systematic review implemented an algorithmic approach to review all the currently available English medical literature on surgical interventions necessitated by COVID-19 thrombotic complications using the preferred reporting items for systematic reviews and meta-analysis principles. A comprehensive search of the medical literature in the "PubMed", "Scopus", "Google Scholar" top 100 results, and archives of Plastic and Reconstructive Surgery was performed using the key words "COVID-19" AND "surgery" AND "thromboembolism" AND "complication". The search string was generated and the records which were not specific about surgical interventions or thrombotic complications due to COVID-19 infection were excluded. Titles and abstracts were screened by two authors and full-text articles were assessed for eligibility and inclusion. Finally, results were further refined to focus on articles that focused on surgical interventions that were necessitated by COVID-19 thrombotic complications.

RESULTS

The database search resulted in the final inclusion of 22 retrospective studies, after application of the inclusion/exclusion criteria. Of the included studies, 17 were single case reports, 3 were case series and 2 were cross sectional cohort studies. All studies were retrospective in nature. Twelve of the reported studies were conducted in the United States of America, with the remaining studies originating from Italy, Turkey, Pakistan, France, Serbia, and Germany. All cases reported in our study were laboratory confirmed SARS-CoV-2 positive. A total of 70 cases involving surgical intervention were isolated from the 22 studies included in this review.

CONCLUSION

There is paucity of data describing the relationship between COVID-19 infection and thrombotic complications necessitating the need for surgical intervention. Intestinal ischemia and acute limb ischemia are amongst the most common thrombotic events due to COVID-19 that required operative management. An overall postoperative mortality of 30% was found in those who underwent operative procedures for thrombotic complications, with most deaths occurring in those with bowel ischemia. Physicians should be aware that despite thromboprophylaxis, severe thrombotic complications can still occur in this patient population, however, surgical intervention results in relatively low mortality apart from cases of ischemic bowel resection.

A gradient boosting tree model for multi-department venous thromboembolism risk assessment with imbalanced data

Venous thromboembolism (VTE) is the world's third most common cause of vascular mortality and a serious complication from multiple departments. Risk assessment of VTE guides clinical intervention in time and is of great importance to in-hospital patients. Traditional VTE risk assessment methods based on scaling tools, which always require rules carefully designed by human experts, are difficult to apply to large-population scenarios since the manually designed rules are not guaranteed to be accurate to all populations. In contrast, with the development of the electronic health record (EHR) datasets, data-driven machine-learning-based risk assessment methods have proven superior predictability in many studies in recent years. This paper uses the gradient boosting tree model to study the VTE risk assessment problem with multi-department data. There exist two distinct characteristics of VTE data collected at the level of the entire hospital: its wide distribution and heterogeneity across multiple departments. To this end, we consider the prediction task over multiple departments as a multi-task learning process, and introduce the algorithm of a task-aware tree-based method TSGB to tackle the multi-task prediction problem. Although the introduction of multi-task learning improves overall across-department performance, we reveal the problem of task-wise performance decline while dealing with imbalanced VTE data volume. According to the analysis, we finally propose two variants of TSGB to alleviate the problems and further boost the prediction performance. Compared with state-of-the-art rule-based and multi-task tree-based methods, the experimental results show the proposed methods not only improve the overall across-department AUC performance effectively, but also ensure the improvement of performance over every single department prediction.

A Prediction Model Based on Blood Biomarker for Mortality Risk in Patients with Acute Venous Thromboembolism

Background

Most studies to date have focused on predicting the risk of venous thromboembolism (VTE), but prediction models about mortality risk in VTE are rarely reported. We sought to develop and validate a multivariable model to predict the all-cause mortality risk in patients with acute VTE in emergency settings.

Methods

A total of 700 patients were included from Qilu Hospital of Shandong University and were randomly assigned into training set (n=490) and validation set (n=210) in an 7:3 ratio. Multivariate logistics regression analysis was performed to identify independent variables and develop a prediction model, which was validated internally using bootstrap method. The discrimination, calibration and clinical utility were evaluated by receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow (HL) test, Kaplan-meier (KM) analysis and decision curve analysis (DCA).

Results

There were 52 patients (10.6%) dying and 437 (89.4%) surviving in training set. Age (odds ratio [OR]: 4.158, 95% confidence interval [CI]: 2.426–7.127), pulmonary embolism (OR: 1.779, 95% CI: 1.124–2.814), platelet count (OR: 0.507, 95% CI: 0.310–0.830), D-dimer (OR: 1.826, 95% CI: 1.133–2.942) and platelet/lymphocyte ratio (OR: 2.166, 95% CI: 1.259–3.727) were independent risk variables associated with all-cause mortality. The model had good predictive capability with an AUC of 0.746 (95% CI: 0.668,0.825), a sensitivity of 0.769 (95% CI: 0.607,0.889), a specificity of 0.672 (95% CI: 0.634,0.707). The validation model had an AUC of 0.739 (95% CI: 0.685,0.793), a sensitivity of 0.690 (95% CI: 0.580,0.787), a specificity of 0.693 (95% CI: 0.655,0.729). The model is well calibrated and the HL test showed a good fit (χ²=5.291, p=0.726, Nagelkerke R²=0.137). KM analysis and DCA showed a good clinical utility of the nomogram.

Conclusion

This study identified independent variables affecting all-cause mortality in patients with acute VTE, and developed a prediction model and provided a nomogram with good prediction capability and clinical utility.

Identification of the Nerve-Cancer Cross-Talk-Related Prognostic Gene Model in Head and Neck Squamous Cell Carcinoma

The incidence of head and neck squamous cell carcinoma (HNSC) is increasing year by year. The nerve is an important component of the tumor microenvironment, which has a wide range of cross-talk with tumor cells and immune cells, especially in highly innervated organs, such as head and neck cancer and pancreatic cancer. However, the role of cancer-nerve cross-talk-related genes (NCCGs) in HNSC is unclear. In our study, we constructed a prognostic model based on genes with prognostic value in NCCGs. We used Pearson’s correlation to analyze the relationship between NCCGs and immune infiltration, microsatellite instability, tumor mutation burden, drug sensitivity, and clinical stage. We used single-cell sequencing data to analyze the expression of genes associated with stage in different cells and explored the possible pathways affected by these genes via gene set enrichment analysis. In the TCGA-HNSC cohort, a total of 23 genes were up- or downregulated compared with normal tissues. GO and KEGG pathway analysis suggested that NCCGs are mainly concentrated in membrane potential regulation, chemical synapse, axon formation, and neuroreceptor-ligand interaction. Ten genes were identified as prognosis genes by Kaplan-Meier plotter and used as candidate genes for LASSO regression. We constructed a seven-gene prognostic model (NTRK1, L1CAM, GRIN3A, CHRNA5, CHRNA6, CHRNB4, CHRND). The model could effectively predict the 1-, 3-, and 5-year survival rates in the TCGA-HNSC cohort, and the effectiveness of the model was verified by external test data. The genes included in the model were significantly correlated with immune infiltration, microsatellite instability, tumor mutation burden, drug sensitivity, and clinical stage. Single-cell sequencing data of HNSC showed that CHRNB4 was mainly expressed in tumor cells, and multiple metabolic pathways were enriched in high CHRNB4 expression tumor cells. In summary, we used comprehensive bioinformatics analysis to construct a prognostic gene model and revealed the potential of NCCGs as therapeutic targets and prognostic biomarkers in HNSC.

Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review

INTRODUCTION

Hospital-acquired thrombosis accounts for a large proportion of all venous thromboembolism (VTE), with significant morbidity and mortality. This subset of VTE can be reduced through accurate risk assessment and tailored pharmacological thromboprophylaxis. This systematic review aimed to determine the comparative accuracy of risk assessment models (RAMs) for predicting VTE in patients admitted to hospital.

METHODS

A systematic search was performed across five electronic databases (including MEDLINE, EMBASE and the Cochrane Library) from inception to February 2021. All primary validation studies were eligible if they examined the accuracy of a multivariable RAM (or scoring system) for predicting the risk of developing VTE in hospitalised inpatients. Two or more reviewers independently undertook study selection, data extraction and risk of bias assessments using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) tool. We used narrative synthesis to summarise the findings.

RESULTS

Among 6355 records, we included 51 studies, comprising 24 unique validated RAMs. The majority of studies included hospital inpatients who required medical care (21 studies), were undergoing surgery (15 studies) or receiving care for trauma (4 studies). The most widely evaluated RAMs were the Caprini RAM (22 studies), Padua prediction score (16 studies), IMPROVE models (8 studies), the Geneva risk score (4 studies) and the Kucher score (4 studies). C-statistics varied markedly between studies and between models, with no one RAM performing obviously better than other models. Across all models, C-statistics were often weak (<0.7), sometimes good (0.7-0.8) and a few were excellent (>0.8). Similarly, estimates for sensitivity and specificity were highly variable. Sensitivity estimates ranged from 12.0% to 100% and specificity estimates ranged from 7.2% to 100%.

CONCLUSION

Available data suggest that RAMs have generally weak predictive accuracy for VTE. There is insufficient evidence and too much heterogeneity to recommend the use of any particular RAM.

PROSPERO REGISTRATION NUMBER

Steve Goodacre, Abdullah Pandor, Katie Sworn, Daniel Horner, Mark Clowes. A systematic review of venous thromboembolism RAMs for hospital inpatients. PROSPERO 2020 CRD42020165778. Available from https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778.