Utilization of the Caprini Score for Risk Stratification of the Arthroplasty Patient in the Prevention of Postoperative Venous Thrombosis

Development of a Predictive Nomogram Model for Early Deep Vein Thrombosis in Postoperative Spontaneous Intracerebral Hemorrhage Patients

Background

This study explores risk determinants for participants' lower extremities deep vein thrombosis (DVT) in the perioperative phase after spontaneous intracerebral hemorrhage (SICH), thereby informing more effective clinical prevention and treatment strategies.

Methods

During the period spanning October 2021 to March 2024, clinical data from 96 participants who received surgical treatment for spontaneous cerebral hemorrhage was analyzed in a retrospective study. Participants were classified into DVT and negative-DVT groups within the first week post-surgery. We used univariate logistic regression and multivariate logistic regression analyses to assess the impact of various clinical variables on DVT. A nomogram model was constructed to forecast the occurrence of early DVT following SICH surgery. The model's performance was assessed and validated using receiver operating characteristic (ROC) curves and bootstrap resampling.

Results

Among the 96 participants, 46 developed DVT. Significant differences were noted in age, D-dimer levels, fibrinogen degradation products, Caprini scores, and total surgical bleeding volume between the groups. Multivariate analysis revealed that Caprini score (the values of OR, 95% CI, and P are 1.962, 1.124-3.424, and 0.018, respectively) and total surgical bleeding volume (the values of OR, 95% CI, and P are 1.010, 1.002-1.018, and 0.017, respectively) were risk variables contributing to DVT occurrence. The area under the receiver operating characteristic curve was 0.918 (95% CI, 0.821-0.988). The calibration curve showed good prediction accuracy.

Conclusion

The Caprini score and total surgical bleeding volume are meaningful self-reliant risk variables contributing to DVT occurrence in postoperative participants with SICH. We have created a straightforward and efficient model to predict early DVT post-SICH surgery. This model serves as a valuable clinical tool for evaluating individual risk and enhancing decision-making processes.

Identifying high-risk groups for deep vein thrombosis after primary total knee arthroplasty using preoperative Caprini scores and D-dimer levels

BACKGROUND

Caprini score and D-dimer are well-recognized markers in deep vein thrombosis (DVT) assessment. However, their utility in guiding post-arthroplasty DVT risk is hampered by susceptibility to various post-operative factors, limiting their effectiveness as reminders. Conversely, these markers exhibit greater stability in the pre-operative setting. Despite this, research on the pre-operative predictive value of Caprini score and D-dimer for DVT following primary total knee arthroplasty (TKA) remains scarce.

METHODS

In a retrospective study, we analyzed data from patients who underwent primary TKA, between August 2015 and December 2022. Upon admission, Caprini scores were assessed, and comprehensive blood panels were obtained from fasting blood samples. For all patients, lower limb vascular Doppler ultrasonography was performed pre-operatively to exclude those with pre-existing DVT, and all patients underwent DVT examination again post-operatively.

RESULTS

Our study included 2,873 patients, averaging 67.98 ± 7.54years, including 676 men and 2,197 women. In this study, 303 (10.55%) patients developed postoperative DVT, and 57 (1.98%) cases presented with lower limb symptoms. DVT incidence in patients with pre-operative Caprini scores of 1-2 (6.50%), 3 (10.28%), and ≥ 4 (18.05%) showed significant differences (P < 0.05). DVT rates were 14.80% in patients with pre-operative D-dimer levels of ≥ 1 mg/L, higher than the 8.98% in those with levels of < 0.5 mg/L, and 10.61% in those with levels 0.5-1 mg/L (P < 0.05). In patients with Caprini scores of 1-2 and D-dimer levels ≤ 0.5 mg/L, the occurrence rate of postoperative DVT was only 5.84%. For patients with Caprini scores ≥ 4 and D-dimer levels ≥ 1.0 mg/L, the postoperative DVT occurrence rate soared to 24.81%, with the OR(odds ratio) was 4.744 compared to the former group.

CONCLUSION

Patients with preoperative higher Caprini scores and D-dimer are more likely to develop DVT after TKA. Additionally, those with a preoperative Caprini score ≥ 4 and D-dimer level ≥ 1.0 mg/L have a significantly increased risk (24.81%) of developing DVT, identifying them as a high-risk group for DVT following TKA. These findings hold significant value for DVT risk stratification in primary TKA patients and the formulation of preoperative interventions to mitigate the risk of DVT.

Development and validation of a novel screening tool for deep vein thrombosis in patients with spinal cord injury: A five-year cross-sectional study

STUDY DESIGN

Cross-sectional study.

OBJECTIVES

Deep vein thrombosis (DVT) presents a significant risk of complication in patients with spinal cord injury (SCI), necessitating accurate screening methods. While the Caprini Risk Assessment Model (Caprini RAM) has seen extensive use for DVT screening, its efficacy remains under scrutiny.

SETTING

First Affiliated Hospital of China University of Science and Technology.

METHODS

We created and evaluated three nomograms for their effectiveness in DVT screening. Model 1 incorporated variables such as age, D-dimer level, red blood cell (RBC) counts, platelet counts, presence of type 2 diabetes mellitus, high blood pressure, mode and level of injury, degree of impairments, and Caprini scores. Model 2 was derived from Caprini scores alone, and Model 3 focused on independent risk factors. We assessed these models using the area under the curve (AUC) of the receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA), employing bootstrap resampling tests (500 iterations) to determine their accuracy, discriminative ability, and clinical utility. Internal validation was performed on a separate cohort. Nomogram was established with well-fitted calibration curves for model 1, 2 and 3(AUC = 0.808, 0.751 and 0.797; 95%CI = 0.76-0.86, 0.70-0.80 and 0.75-0.84; respectively), indicating model 1 outperformed the others in prediction DVT risk, followed by model 3 and 2. These findings were consistent in the validation cohort, with DCA further corroborating our conclusions.

CONCLUSION

A nomogram integrating clinical data with Caprini RAM provides a superior option for DVT screening in SCI patients within rehabilitation settings, outperforming Caprini RAM.

Construction and verification of a machine learning-based prediction model of deep vein thrombosis formation after spinal surgery

BACKGROUND

Deep vein thromboembolism (DVT) is a common postoperative complication with high morbidity and mortality rates. However, the safety and effectiveness of using prophylactic anticoagulants for preventing DVT after spinal surgery remain controversial. Hence, it is crucial to predict whether DVT occurs in advance following spinal surgery. The present study aimed to establish a machine learning (ML)-based prediction model of DVT formation following spinal surgery.

METHODS

We reviewed the medical records of patients who underwent elective spinal surgery at the Third Affiliated Hospital of Zunyi Medical University (TAHZMU) from January 2020 to December 2022. We ultimately selected the clinical data of 500 patients who met the criteria for elective spinal surgery. The Boruta-SHAP algorithm was used for feature selection, and the SMOTE algorithm was used for data balance. The related risk factors for DVT after spinal surgery were screened and analyzed. Five ML algorithm models were established. The data of 150 patients treated at the Affiliated Hospital of Zunyi Medical University (AHZMU) from July 2023 to October 2023 were used for external verification of the model. The area under the curve (AUC), geometric mean (G-mean), sensitivity, accuracy, specificity, and F1 score were used to evaluate the performance of the models.

RESULTS

The results revealed that activated partial thromboplastin time (APTT), age, body mass index (BMI), preoperative serum creatinine (Crea), anesthesia time, rocuronium dose, and propofol dose were the seven important characteristic variables for predicting DVT after spinal surgery. Among the five ML models established in this study, the random forest classifier (RF) showed superior performance to the other models in the internal validation set.

CONCLUSION

Seven preoperative and intraoperative variables were included in our study to develop an ML-based predictive model for DVT formation following spinal surgery, and this model can be used to assist in clinical evaluation and decision-making.

Dissecting the rationale for thromboprophylaxis in challenging surgical cases

Pulmonary embolism (PE) is a leading preventable cause of death in surgical patients, and rates of fatal PE are increasing. Individual assessment, to balance the risks of thrombosis and bleeding, is the key to providing appropriate prophylaxis. The risk assessment process includes use of evidence-based guidelines, literature published since the latest guidelines, large registries, and risk scoring systems together with clinical experience and judgment. Risk assessment is a dynamic process and needs to be updated both during the hospital stay and just prior to discharge since clinical events may change the level of risk. The final assessment may identify patients who require ongoing anticoagulant prophylaxis after discharge. The Caprini risk score is widely used in surgical patients and is a composite of the number of risk factors and their relative weights. The Caprini risk score set point for risk levels requiring anticoagulant prophylaxis varies depending on the type of surgical procedure, surgical population, and number of risk factors. Mandatory implementation of evidence-based care pathways is helpful in lowering PE-related mortality. This review presents several challenging cases, emphasizing the importance of employing all available assessment tools, including dynamic assessment of risk during hospitalization. Finally, the limitations of evidence-based guidelines in complex scenarios and the need to employ all available tools to properly protect very high-risk patients are emphasized.

The Validation and Modification of the Caprini Risk Assessment Model for Evaluating Venous Thromboembolism after Joint Arthroplasty

BACKGROUND

 The Caprini risk assessment model (RAM) is the most commonly used tool for evaluating venous thromboembolism (VTE) risk, a high score for arthroplasty can result in patients being classified as high risk for VTE. Therefore, its value in post-arthroplasty has been subject to debate.

METHODS

 Retrospective data were collected from patients who underwent arthroplasty between August 2015 and December 2021. The study cohort included 3,807 patients, all of whom underwent a thorough evaluation using Caprini RAM and vascular Doppler ultrasonography preoperatively.

RESULTS

 A total of 432 individuals (11.35%) developed VTE, while 3,375 did not. Furthermore, 32 (0.84%) presented with symptomatic VTE, while 400 (10.51%) were detected as asymptomatic. Additionally, 368 (9.67%) VTE events occurred during the hospitalization period, and 64 (1.68%) cases were detected during postdischarge follow-up. Statistical analysis revealed significant differences between the VTE and non-VTE groups in terms of ages, blood loss, D-dimer, body mass index >25, visible varicose veins, swollen legs, smoking, history of blood clots, broken hip, percent of female, hypertension, and knee joint arthroplasty (p < 0.05). The Caprini score was found to be significantly higher in the VTE group (10.10 ± 2.23) compared with the non-VTE group (9.35 ± 2.14) (p < 0.001). Furthermore, there was a significant correlation between the incidence of VTE and the Caprini score (r = 0.775, p = 0.003). Patients with a score ≥9 are at a high-risk threshold for postoperative VTE.

CONCLUSION

 The Caprini RAM shows a significant correlation with the occurrence of VTE. A higher score indicates a greater likelihood of developing VTE. The score ≥9 is at particularly high risk of developing VTE.

Comparative study of two models predicting the risk of deep vein thrombosis progression in spinal trauma patients after operation

OBJECTIVE

Patients with preoperative deep vein thrombosis (DVT) exhibit a notable incidence of postoperative deep vein thrombosis progression (DVTp), which bears a potential for silent, severe consequences. Consequently, the development of a predictive model for the risk of postoperative DVTp among spinal trauma patients is important.

METHODS

Data of 161 spinal traumatic patients with preoperative DVT, who underwent spine surgery after admission, were collected from our hospital between January 2016 and December 2022. The least absolute shrinkage and selection operator (LASSO) combined with multivariable logistic regression analysis was applied to select variables for the development of the predictive logistic regression models. One logistic regression model was formulated simply with the Caprini risk score (Model A), while the other model incorporated not only the previously screened variables but also the age variable (Model B). The model's capability was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, and receiver operating characteristic (ROC) curve. Nomograms simplified and visually presented Model B for the clinicians and patients to understand the predictive model. The decision curve was used to analyze the clinical value of Model B.

RESULTS

A total of 161 DVT patients were enrolled in this study. Postoperative DVTp occurred in 48 spinal trauma patients, accounting for 29.81% of the total patient enrolled. Model A inadequately predicted postoperative DVTp in spinal trauma patients, with ROC AUC values of 0.595 for the training dataset and 0.593 for the test dataset. Through the application of LASSO regression and multivariable logistic regression, a screening process was conducted for seven risk factors: D-dimer, blood platelet, hyperlipidemia, blood group, preoperative anticoagulant, spinal cord injury, lower extremity varicosities. Model B demonstrated superior and consistent predictive performance, with ROC AUC values of 0.809 for the training dataset and 0.773 for the test dataset. According to the calibration curves and decision curve analysis, Model B could accurately predict the probability of postoperative DVTp after spine surgery. The nomograms enhanced the interpretability of Model B in charts and graphs.

CONCLUSIONS

In summary, we established a logistic regression model for the accurate predicting of postoperative deep vein thrombosis progression in spinal trauma patients, utilizing D-dimer, blood platelet, hyperlipidemia, blood group, preoperative anticoagulant, spinal cord injury, lower extremity varicosities, and age as predictive factors. The proposed model outperformed a logistic regression model based simply on CRS. The proposed model has the potential to aid frontline clinicians and patients in identifying and intervening in postoperative DVTp among traumatic patients undergoing spinal surgery.

A prediction nomogram for deep venous thrombosis risk in patients undergoing primary total hip and knee arthroplasty: a retrospective study

INTRODUCTION

Deep venous thrombosis (DVT) prediction after total hip and knee arthroplasty remains challenging. Early diagnosis and treatment of DVT are crucial. This research aimed to develop a nomogram for early DVT prediction.

METHODS

A total of 317 patients undergoing primary total hip and knee arthroplasty in Sun Yat-sen Memorial Hospital were enrolled between May 2020 and September 2022. Data from May 2020 to February 2022 were used as the development datasets to build the nomogram model (n = 238). Using multivariate logistic regression, independent variables and a nomogram for predicting the occurrence of DVT were identified. Datasets used to validate the model for internal validation ranged from March 2022 to September 2022 (n = 79). The nomogram's capacity for prediction was also compared with the Caprini score.

RESULTS

For both the development and validation datasets, DVT was found in a total of 38 (15.97%) and 9 patients (11.39%) on post-operative day 7 (pod7), respectively. 59.6% patients were symptomatic DVT (leg swelling). The multivariate analysis revealed that surgical site (Knee vs. Hip), leg swelling and thrombin-antithrombin complex (TAT) were associated with DVT. The previously indicated variables were used to build the nomogram, and for the development and validation datasets, respectively. In development and validation datasets, the area under the receiver operating characteristic curve was 0.836 and 0.957, respectively. In both datasets, the predictive value of the Nomogram is greater than the Caprini score.

CONCLUSIONS

A proposed nomogram incorporating surgical site (Knee vs. Hip), leg swelling, and thrombin antithrombin complex (TAT) may facilitate the identification of patients who are more prone to develop DVT on pod7.

A Comparison of the Caprini Score With an Institutional Risk Assessment Tool for Prediction of Venous Thromboembolism After Total Joint Arthroplasty at an Urban Tertiary Care Health Safety Net Hospital

Background

Patients undergoing total joint arthroplasty (TJA) are at increased risk for venous thromboembolism (VTE). Prediction tools such as the Caprini Risk Assessment Model (RAM) have been developed to identify patients at higher risk. However, studies have reported heterogeneous results when assessing its efficacy for TJA. Patients treated in an urban health safety net hospital have increased medical complexity, advanced degenerative joint disease, and severe disability prior to TJA increasing the risk of VTE. We hypothesize that use of a tool designed to account for these conditions-the Boston Medical Center (BMC) VTE score-will more accurately predict VTE in this patient population.

Methods

A retrospective case-control study was performed including subjects 18 years of age and older who underwent primary or revision TJA in an urban academic health safety net hospital. Patients with hemiarthroplasties, simultaneous bilateral TJA, and TJA after acute trauma were excluded. A total of 80 subjects were included: 40 who developed VTE after TJA (VTE+) and 40 who did not develop VTE (controls). Subjects were matched by age, gender, and surgical procedure.

Results

There was a statistically significant difference between the mean BMC VTE score for VTE+ and controls (4.40 and 3.13, respectively, P = .036). Conversely, there was no statistical difference between the mean Caprini scores for VTE+ and controls (9.50 and 9.35, respectively, P = .797).

Conclusions

In a health safety-net patient population, an institutional RAM-the BMC VTE score-was found to be more predictive of VTE than the modified Caprini RAM following TJA. The BMC-VTE score should be externally validated to confirm its reliability in VTE prediction in similar patient populations.

Resumption of Elective Orthopaedic Surgery in the US Epicenter of COVID-19: Overcoming the Continuous Challenges

On 1 March 2020, New York State confirmed its first case of COVID-19. An explosive progression of hospitalizations ensued, and all elective surgeries were cancelled between 23 March and 13 May 2020 per federal and state mandate. Upon return to elective surgery in May, 2020, the hospital found itself navigating uncharted territory. The unpredictability of the post-pandemic environment has required the healthcare team to constantly reassess and revise processes to ensure optimal patient outcomes, as well as safe practices for staff providing perioperative care. Health care professionals must continue to remain adaptable and amenable to constant change.