The Caprini RAM was originally developed and extensively validated for surgical patients, and its strongest predictive performance has consistently been demonstrated in perioperative and postoperative settings. The Caprini RAM, introduced by Joseph Caprini in the early 1990s, remains the most extensively validated tool for estimating the risk of perioperative VTE. It assigns weighted points to evidence-based risk factors, including patient age, prior VTE, cancer status, and the type and duration of surgery, to generate an aggregate score that guides prophylaxis decisions. The original 20-item version, validated in general surgical cohorts, stratifies patients into low (0–1), moderate (2–4), and high risk (≥5) categories for postoperative VTE (10). Subsequent updates expanded the inventory to 36 variables applicable across multiple surgical specialties. Added factors include varicose veins, inflammatory bowel disease, morbid obesity, cardiorespiratory disease, insulin-dependent diabetes, exposure to hormonal or chemotherapy agents, recurrent pregnancy loss, recent blood transfusion, and smoking history (15).

Across surgical populations, the Caprini score exhibits robust diagnostic accuracy, with a sensitivity of approximately 96%, a specificity of approximately 92%, and positive and negative predictive values of roughly 93% and 95.5%, respectively (38), establishing it as a key element of personalized prophylaxis strategies. Its widespread use, however, is limited by the necessity to assess 36 individual variables (15), the lack of universally recognized risk-category thresholds, and a scarcity of validation data in medical or pediatric inpatient settings.

The Padua Prediction Score (PPS) was specifically developed to assess VTE risk in acutely ill hospitalized medical patients and is not intended for routine use in surgical or obstetric populations. It was introduced by Barbar and colleagues in 2010 to stratify acutely ill medical inpatients based on 11 weighted clinical variables (total score: 0–20); a score of 4 or higher denotes a high thrombotic risk. In the 1,180-patient derivation cohort, omission of prophylaxis in the high-risk stratum translated into an approximately 11-fold increase in symptomatic VTE, underscoring the model's discriminatory capacity (11). External validations, however, have yielded heterogeneous results. In septic medical patients, the PPS failed to predict in-hospital or one-year VTE, although higher scores were independently associated with inpatient mortality (39). Comparative surgical data suggest the score retains good discrimination for postoperative events (38). Among lung-cancer in-patients receiving immune checkpoint inhibitors, the PPS performs on par with the Caprini model (40). Conversely, studies in obstetric and peripartum cohorts reveal poor sensitivity, highlighting the paucity of validation in this population (41).

The Padua RAM scoring system comprises 11 risk factors for VTE, with a total score ranging from 0 to 20 points, depending on the number and weight of the identified risk factors (Table 1). Patients with a score of 4 or higher are classified as high-risk, while those with a score below four are classified as low-risk (11).

Despite its broad validation and ease of integration into electronic health record systems (42), its utility across all hospitalized patients remains limited due to its lack of validity in specific populations, such as pregnant (16) and cancer patients (17). Additionally, the PPS has not been effectively validated in certain racial groups, such as the Chinese population (43). Furthermore, it may not be suitable for predicting VTE risk in patients with acute respiratory conditions (44). One study showed that the PPS did not improve clinical outcomes when restricted to patients admitted for 72 h or more, after excluding upper-extremity DVT as an outcome, and after including all-cause mortality in a composite outcome (45). These findings suggest that its lack of dynamic risk adjustment and limited generalizability necessitate further evaluation to enhance its predictive accuracy for VTE risk assessment.

The IMPROVE model was derived from large cohorts of hospitalized medical patients and is designed to support VTE risk stratification in medical inpatient settings. It was developed from the multinational International Medical Prevention Registry on VTE (IMPROVE), which enrolled 15,156 acutely ill medical inpatients across 52 hospitals in 12 countries between 2002 and 2006. The IMPROVE RAM enables clinicians to quantify baseline and evolving thrombotic risk. The four-item IMPROVE-Predictive Score is calculated upon admission (previous VTE = 3 points, known thrombophilia = 3, age over 60 years = 1, active cancer =  1); scores of 0–1, 2–3, and ≥4 indicate low, intermediate, and high risk, respectively, with the high-risk group (Table 2) showing a 90-day VTE incidence of approximately 5.7% (18, 19, 46). Risk can then be adjusted in the hospital by adding three dynamic variables: lower-limb paralysis (2 points), immobilization for 7 days or more (1 point), and ICU/CCU admission (1 point), resulting in the seven-factor IMPROVE-Associative Score (range: 0–12) (Table 2) (19, 20). External validation by Cobben et al. confirmed that this combined seven-factor model demonstrated the best discriminative performance in their cohort (AUC 0.66, 95% CI: 0.56–0.75), supporting its clinical utility while highlighting the need for further refinement in various settings (47).

The IMPROVE RAM was derived from a large, geographically diverse registry of acutely ill medical patients and has since been embedded in several electronic health record (EHR) order sets. However, it's essential to note that the IMPROVE RAM was not designed to assess how thromboprophylaxis after admission affects the occurrence of VTE events, nor to determine how VTE prevention might influence the model's results, as patients were not randomly assigned to receive prophylaxis. Another limitation could be the reliance on clinical endpoints rather than ongoing monitoring of VTE, which may have led to an underestimation of VTE cases (18). Additionally, the method doesn’t account for new risk factors or biomarkers, such as D-dimer, leukocyte, or platelet counts.

Gibson and colleagues evaluated the association between D-dimer levels above twice the upper limit of normal (ULN) and the IMPROVE score's ability to predict nonfatal pulmonary embolism, VTE-related death, or symptomatic DVT in a cohort of 7,441 hospitalized, medically ill patients from the APEX trial. When D-dimer exceeded 2x ULN, an extra 2 points were added to the IMPROVE score, creating the IMPROVEDD score (Table 2). Baseline D-dimer levels were independently linked to symptomatic VTE over 77 days (21). In hospitalized patients with medical conditions, the modified IMPROVE VTE risk score, which includes high D-dimer levels as a biomarker, identified a group at nearly three times higher risk for VTE. For these patients, extended thromboprophylaxis with rivaroxaban for up to 35 days provides a notable benefit (22).

Gibson et al. emphasize that the IMPROVE clinical variables, combined with a D-dimer level at least twice the ULN, serve as independent predictors of in-hospital VTE. Since this evidence is based solely on the APEX trial, in which all participants received either standard enoxaparin or extended betrixaban, its relevance to settings with different or no prophylactic treatments is uncertain (21, 22). Importantly, elevated D-dimer levels may reflect inflammation, malignancy, or acute illness rather than thrombosis alone and should therefore be interpreted only within a validated multivariable framework.

The Geneva score is a diagnostic risk-assessment model designed to estimate the clinical probability of pulmonary embolism in patients with suspected VTE, primarily in emergency and outpatient settings. Wicki et al. first introduced the Geneva score in 2001 within a single-center Swiss cohort of 1,090 patients from the emergency department (28). The 0- to 16-point scale assigns weighted values to eight objective variables: age ≥ 60 years, surgery within the past 4 weeks, prior DVT/PE, heart rate > 100 bpm, PaO₂ < 82 mmHg, PaCO₂ < 39 mmHg, and chest radiograph evidence of plate-like atelectasis or hemidiaphragm elevation. The resulting totals categorize clinical pre-test probability as low (0–4), intermediate (5–8), or high (>8) (Table 3). In 2006, Le Gal and colleagues simplified the score by removing chest radiography and arterial blood-gas measurements, resulting in the revised Geneva score (48). This version relies on eight easily accessible clinical variables: heart rate ≥ 75 bpm, age > 65 years, unilateral lower-limb pain with tenderness or edema, hemoptysis, prior DVT/PE, recent surgery or lower-limb fracture (<4 weeks), and active cancer, stratifying pulmonary embolism probability as low (0–3), intermediate (4–10), or high (≥11) points (Table 3). Later, in 2008, Klok et al. introduced the simplified revised Geneva score, which uses the same eight clinical variables but assigns one point to each (heart rate ≥ 95 bpm receives two points). Scores of 0–1, 2–4, and 5–7 indicate low (approximately 8%), intermediate (approximately 29%), and high (approximately 64%) pre-test probabilities, respectively. A binary cutoff of ≤2 vs. ≥3 (“PE unlikely” vs. “PE likely”) supports D-dimer–guided exclusion strategies (Table 3) (29). External validation has confirmed the model's strong discrimination and calibration; however, clinical use remains inconsistent, partly due to alert fatigue and the earlier, more cumbersome versions of the Geneva score, which may hinder the implementation of decision-support systems (30).

The Geneva RAMs streamline pre-test assessments and can be combined with D-dimer testing to safely exclude PE without imaging. Prospective validation in the ADJUST-PE management trial confirmed that the simplified RAM maintained safety and accuracy in 1,621 outpatients (30). However, both revised and simplified versions, originating from emergency ward cohorts, show reduced discrimination in general medical inpatients (AUC ≈ 0.58 and positive predictive value ≈ 3% in a 2024 head-to-head comparison of risk models) (31), limiting their usefulness for hospital-acquired VTE surveillance or thromboprophylaxis decisions. Their use in ward-based or perioperative populations should await validation specific to those groups.

The Wells score was developed to estimate the pretest probability of suspected VTE and is intended for diagnostic decision-making rather than routine thromboprophylaxis risk assessment. It was first proposed in 1995 and refined in 1997, which translates bedside findings into a quantitative pre-test probability (12). The updated model assigns +1 point for each of nine predictors: active cancer, paralysis/paresis or recent plaster immobilization of a lower limb, recent bed-rest > 3 days or major surgery within the previous 12 weeks, localized tenderness along the deep-venous system, entire leg swelling, calf circumference ≥ 3 cm larger than the asymptomatic leg, unilateral pitting oedema, collateral superficial (non-varicose) veins, and previous DVT, while an alternative diagnosis at least as likely as DVT carries −2 points. Totals stratify patients into low (≤0), intermediate (1–2), and high (≥3) probability groups, corresponding to observed proximal DVT prevalences of roughly 5%, 17%, and 53%, respectively (Table 4) (12, 49). The original well model was revised in 2003 to become the modified Wells. Three modifications were made: Twelve weeks were added to the recovery period following major surgery, for previously confirmed DVT, a tenth predictor was added and assigned one point, and the associated risk was divided into likely (≥2) categories and DVT unlikely (<2 points) (Table 4) (23). In 2008, Gibson et al. simplified the PE Wells rule by retaining the original seven predictors and assigning each a point, with a total score of ≤1 classifying PE as unlikely and a score > 1 as likely (Table 4). In their prospective cohort, the 3-month VTE rate after a negative D-dimer was nearly the same as that of the conventional and simplified rules (0.3% vs. 0.5%), confirming non-inferiority (24). A later single-center series by Al-Khafaji et al. showed that the Wells framework maintained excellent discrimination for DVT in both emergency department and ward patients (AUC ≈ 0.86) (25). Finally, Hendriksen and colleagues compared the original, modified, and simplified Wells rules with the revised and simplified Geneva scores in 598 outpatients. Failure rates after a normal D-dimer were lower with the Wells variants (1.2%–1.5%) than with either of the Geneva algorithms (2.7%–3.1%) (26).

In trauma patients, DVT can be reliably excluded with a Wells score of less than 1 (27). A modified Wells score, combined with a negative D-dimer test, can confidently exclude DVT in outpatients or individuals in primary care settings, as indicated by Geersing et al. (50). However, the Wells score exhibits limited effectiveness in patients with distal DVT, older patients, and those with a history of prior DVT (51–53).

The Kucher RAM was created as a real-time, electronic alert embedded in the EHR to flag medical inpatients at an elevated risk of VTE (13). “Kucher alert” improved the prescribing of prophylactic anticoagulation and reduced 90-day symptomatic VTE by ∼approximately 40% (13). Sustained benefit was later confirmed in an observational follow-up, with a halving of VTE incidence when the alert system remained active (54). The RAM assigns weighted points to eight variables: prior VTE, active cancer, known hypercoagulability (3 points each); recent major surgery (2 points); and age > 75 years, body-mass index > 29 kg m², bed rest ≥ 3 days, or estrogen/hormone therapy (1 point each). A cumulative score of 4 or higher triggers a “Kucher alert”, prompting clinicians to initiate pharmacological or mechanical prophylaxis; lower totals stratify patients into intermediate- and low-risk categories.

Subsequent external validation in a 20-hospital U.S. cohort showed that the Kucher RAM had the weakest discrimination among five competing scores (c-statistic approximately 0.55). This raises concerns about its generalizability outside the environment in which it was developed. The study also found that most alerts were ignored, a pattern attributed to alert fatigue caused by the indiscriminate firing of alerts for all medical inpatients (55). Current guidance now recommends limiting e-alerts to truly high-risk patients and pairing them with a parallel bleed-risk prompt, an element missing from the original design (56).