In a further substudy, we sought to compare the proportion of hospital patients recommended for pharmacological thromboprophylaxis using multiple RAMs identified by our systematic review in workstream one, alongside current practice. We included RAMs with attempted external validation for this comparison. We specifically aimed to determine the following parameters for a potential future implementation study:
- The current proportion of hospital inpatients receiving pharmacological prophylaxis in a UK NHS setting after local risk assessment, using the Department of Health tool.
- The variable proportion of patients who would potentially receive prophylaxis if alternative RAMs were used.
We conducted this substudy in accordance with international EQUATOR guidelines. A completed STROBE reporting checklist was used throughout to inform design, conduct and analysis of this observational cohort study and has been submitted with the report.
Methods
We approached four sites engaging with the wider Venous ThromboEmbolism Assessment Methods (VTEAM) project to participate in this substudy. We originally intended to explore methods of efficient data collection for risk factors across two sites through an iterative process, then further pilot a final data collection tool across all sites. However, only one site maintained an EHR with prospective capture of individual VTE risk characteristics following clinical assessment. Other sites lacked the IT infrastructure to support efficient data collection, using either paper proformas to record risk assessment or dichotomous electronic methods [recording only the capture of a risk assessment (yes/no) and the prescribing intention, as shown in Appendix 3, Figure 28].
We considered adaptive design during the project and developing a paper case note review process, but encountered multiple barriers to delivery of workstream 2 during the project timeline; access to clinical settings was limited by infection control measures during the pandemic; redeployment of clinically trained research staff limited our available resources; pandemic research was rightly prioritised by local research teams; and NHS service pressures impacted on the project. In addition, working from home guidance further limited the ability to retrieve paper case notes or develop an efficient data collection tool. As such, we focused efforts on data capture from a single site which used a comprehensive EHR with contemporaneous data collection on risk factors at the point of assessment.
Population
We identified a consecutive cohort of general medical and surgical patients requiring hospitalisation during the calendar year from 1 January to 31 December 2019. We chose 2019 because of concern that patients admitted during the COVID-19 pandemic might have been assessed for VTE prophylaxis differently to prior guidelines.141 We collated data on all risk assessments performed prospectively at the point of hospital admission, then scrambled episodes into randomly assorted batches of 50 to ensure diversity in speciality presentation and mitigate potential seasonal bias. We excluded all patients with an index presentation of suspected VTE, anticoagulated patients and patients with a hospital stay < 24 hours as they were unlikely to require risk assessment or thromboprophylaxis. We also excluded patients requiring critical care admission, children (< 16) and pregnant/post-partum patients due to differential risk, in keeping with the wider study protocol.
Study design
A single-centre observational cohort study, conducted at Salford Royal NHS Foundation Trust. We extracted initial clinical data on patient episodes using hospital admission records and VTE risk assessments completed prospectively by clinical teams during the provision of routine care. All risk assessments were captured through a structured note within the electronic healthcare record, with granular data capture accessible through business intelligence. The structured note mirrors the Department of Health VTE Risk assessment tool (see Appendix 3, Figure 28).
We identified additional necessary variables a priori to enable further completion and comparison of 6 prespecified RAMs for each patient episode, in addition to the Department of Health tool. These variables are listed in Table 17. We subsequently undertook case note review for each patient episode through shared primary and secondary care EHRs up to 90 days following hospital discharge. We extracted data points necessary to facilitate retrospective completion of the Padua, Caprini, Geneva, Kucher, IMPROVE predictive and IMPROVE associative RAMs. We also extracted descriptive data on relevant clinical outcomes, such as the real-world use of pharmacological thromboprophylaxis, subsequent diagnosis of HAT, length of stay and survival to hospital discharge. Data were also extracted on episodes of major and CRNMB as per internationally agreed definitions.79,80 Data extractors were trained in identification of these variables/outcomes and followed a detailed workflow diagram (see Appendix 3, Figure 29). Data abstractors were blinded to the analysis plan.
| DOH RAM | Padua RAM | IMPROVEAssociative RAM | Geneva RAM | Caprini RAM | Kucher RAM | IMPROVEPredictive RAM |
|---|---|---|---|---|---|---|
| Age | Cardiac failure | Paralysis of thelower extremities | Myeloproliferativesyndrome | Sex | Nil additional | Nil additional |
| Active cancer | Respiratory failure | Immobilisation> 7 days | Nephroticsyndrome | Acute pneumonia(< 1/12) | ||
| Dehydration | Acute myocardialinfarction (< 1/12) | Recent travel > 6 hours | Immobilisingplaster cast | |||
| Obesity | Acute ischaemicstroke (< 3/12) | Chronic venousinsufficiency | Multiple trauma(< 1/12) | |||
| One or more significant medical comorbidities | Acute infectiousdisease (< 1/12) | Current swollenlegs | ||||
| Personal history of first-degree relative with a VTE history | Acute rheumatologicaldisorder (< 1/12) | Current centralvenous access | ||||
| Use of HRT | Inflammatorybowel disease | |||||
| Use of oestrogen-containing COCP | Other risk factor | |||||
| Varicose veins with phlebitis | Major surgery(< 1/12) | |||||
| Pregnancy or < 6 weeks post-partum | Hip, pelvis or legfracture (< 1/12) | |||||
| Mobility limited > 3/7 | ||||||
| Acute surgical admission | ||||||
| Critical care admission | ||||||
| Specific surgical features |
Statistical analysis methods
The primary analysis was a comparison of the proportion of patients within the cohort in whom pharmacological thromboprophylaxis would be recommended during hospitalisation, by each RAM. We applied conventional prescribing thresholds for each RAM to the cohort in order to estimate the proportion of patients who would require medication, prior to any assessment of bleeding risk. Prescribing thresholds were taken from author recommendations, international guidelines and nationally endorsed summary tools for practical use.8,15,16,28,34,49,53,59,142–149 We used ordinal scores to create distribution plots, clustered histograms and to facilitate descriptive analysis. We used dichotomous prescribing thresholds (as above) to calculate proportions with 95% confidence intervals and allow head-to-head RAM comparison. We used the Wilson method to estimate confidence intervals around a proportion, adapted by Newcombe.139
Missing data were assumed to be negative (risk factor not present) for the primary analysis. A sensitivity analysis was planned with best-case-worse-case imputation for missing data when calculating the percentage of patients who would meet the criteria for prescribing thromboprophylaxis. We estimated that 80% of admitted patients would be classified as eligible for pharmacological thromboprophylaxis, according to the Department of Health VTE risk assessment tool.8,145 A sample of 250 patients would, therefore, be sufficient to provide estimates of prescribing with a confidence interval range approaching 10%. We proposed a data review following completion of at least 500 case episodes (10 batches).
Ethical aspects
The study received a favourable opinion from the Proportionate Review Sub-committee of the London – West London & GTAC REC and approval from the HRA and Care Research Wales (HCRW) on 18 September 2019 (reference 19/LO/1303, IRAS project ID 262220).
Patient and public involvement
Representatives of two PPI groups, Thrombosis UK and SECF joined the research team and were involved in developing the proposal and undertaking the study.
The SECF is a patient and public representative group with an interest in emergency care research. The forum has provided PPI for many emergency care research projects over the last 10 years (see https://secf.org.uk/ and https://emj.bmj.com/content/33/9/665). Thrombosis UK is a charity that aims to identify, inform and partner with the NHS, healthcare providers and individuals to work to improve prevention of VTE and the management and care of VTE events (see www.thrombosisuk.org/).
The PPI members were involved in determining the study design and ensuring that the proposal addressed the needs of patients and the NHS, while respecting the needs of potential participants. Their input regarding the importance of providing VTE prophylaxis for potential participants of any prospective cohort study and the need for such a study to yield reliable findings was instrumental in determining our approach to answering the research question. The PPI members also provided PPI at project management meetings and, where required, in day-to-day running of the project. The members used meetings and surveys of their wider PPI groups to enhance PPI in the project.
Results
Comparative risk assessment and prescribing outcomes
We analysed 543 hospital episodes with VTE risk assessment performed during 2019. Two-hundred and eighty-nine episodes met exclusion criteria, leaving 254 episodes suitable for data extraction and comparative analysis.
Distribution plots of ordinal scoring for each RAM are shown in Figure 24. The Caprini and Department of Health RAMs appeared to be normally distributed, with the former showing the widest dispersion. All others were skewed to the left, indicating a higher attribution of low risk.
FIGURE 24 Histograms for ordinal score (x-axis) by RAM using patient numbers (y-axis), with recommended prescribing thresholds (dashed blue lines). Source: National Institute for Health and Care Excellence8; Barbar et al.15; Blondon et al.28; Caprini et al.142; Chopard et al.143; Kucher et al.146; Spyropoulos et al.148 (Figure not reproduced here; see the original article.)
Overall recommendations for prescribing varied substantially between RAMs, ranging from 13% for the IMPROVE associative score to 91% for the Department of Health VTE risk assessment tool. Variation persisted throughout medical (15–92%) and surgical subgroups (10–88%). Key results and estimates of recommended prescribing by speciality (with 95% CIs) are shown in Table 18. Of note, all RAMs recommended higher rates of prescribing in medically unwell patients within this patient cohort. A bar and dot chart of prescribing recommendations with comparative 95% confidence intervals is shown in Figure 25. The Department of Health VTE risk assessment tool was an outlier within this cohort, recommending pharmacological thromboprophylaxis for over 90% of patients with a confidence interval that did not overlap those of any other RAM.
| RAM and published prescribing threshold | All patients(n = 254) | Medical admissions(n = 143) | Surgical admissions(n = 111) |
|---|---|---|---|
| Department of Health VTE risk assessment tool | |||
| Median value (IQR) | 2 (1–3) | 3 (2–4) | 2 (1–3) |
| Prescribing threshold (score ≥ 1)a | 230 (91%, 95% CI 86.3 to 93.6) | 132 (92%, 95% CI 86.8 to 95.7) | 98 (88%, 95% CI 81.0 to 93.0) |
| Padua score | |||
| Median value (IQR) | 2 (1–4) | 3 (1–4) | 1 (0–3) |
| Prescribing threshold (score ≥ 4) | 77 (30%, 95% CI 25.0 to 36.2) | 56 (39%, 95% CI 31.5 to 47.3) | 21 (19%, 95% CI 12.7 to 27.2) |
| Caprini score | |||
| Median value (IQR) | 5 (3–7) | 6 (4–8) | 4 (2–5) |
| Prescribing threshold (score ≥ 5) | (56%, 95% CI 49.8 to 61.9) | 96 (67%, 95% CI 59.1 to 74.3) | 46 (41%, 95% CI 32.7 to 50.7) |
| Geneva risk score | |||
| Median value (IQR) | 2 (1–4) | 3 (2–5) | 2 (0–3) |
| Prescribing threshold (score ≥ 3) | 111 (44%, 95% CI 37.7 to 49.9) | 81 (57%, 95% CI 48.5 to 64.5) | 30 (27%, 95% CI 19.6 to 36.0) |
| Kucher score | |||
| Median value (IQR) | 1 (1–2) | 1 (1–2) | 1 (0–2) |
| Prescribing threshold (score ≥ 4) | 26 (10%, 95% CI 7.1 to 14.6) | 16 (11%, 95% CI 7.0 to 17.4) | 10 (9%, 95% CI 5.0 to 15.8) |
| IMPROVE predictive score | |||
| Median value (IQR) | 1 (0–1) | 1 (0.5–1) | 0 (0–1) |
| Prescribing threshold (score ≥ 1) | 155 (61%, 95% CI 54.9 to 66.8) | 107 (75%, 95% CI 67.1 to 81.2) | 48 (43%, 95% CI 34.4 to 52.5) |
| IMPROVE associative score | |||
| Median value (IQR) | 1 (0–1) | 1 (1–1) | 0 (0–1) |
| Prescribing threshold (score ≥ 3) | 32 (13%, 95% CI 9.1 to 17.2) | 21 (15%, 95% CI 9.8 to 21.4) | 11 (10%, 95% CI 5.6 to 16.9) |
FIGURE 25 A visual comparison of prescribing proportions per RAM, with 95% CIs. Source: National Institute for Health and Care Excellence8; Barbar et al.15; Blondon et al.28; Caprini et al.142; Chopard et al.143; Kucher et al.146; Spyropoulos et al.148 (Figure not reproduced here; see the original article.)
On descriptive analysis, the variables used by the Department of Health tool with the highest prevalence within our cohort were as follows: one or more significant medical co-morbidities (76%); age > 60 (57%); dehydration (38%); significantly reduced mobility (29%); obesity (20%). These 5 variables were responsible for > 90% cumulative prescribing decisions within the cohort, with negligible addition from the other 14 variables.
All tools were subject by missing data with up to 12% of patients being partly scored, largely due to BMI being unavailable in 22 (9%) of patients. This affected all scores other than the two IMPROVE scores which were scored in all bar four (2%) patients. The impact of this was minimal in terms of the percentage of patients that would be recommended for thromboprophylaxis, since the missing data generally could not overturn a ‘do not recommend’ into ‘recommend’. For the Padua score, a further seven patients would meet the cut-off of 4 points if all missing data were assumed to be abnormal, taking the percentage from recommended from 30% to 33%. The equivalent assumption on other tools led to increases of less than 2%.
Temporary reference list for Figures 24 and 25 and Table 18:
- Barbar S, Noventa F, Rossetto V, Ferrari A, Brandolin B, Perlati M, et al. A risk assessment model for the identification of hospitalized medical patients at risk for venous thromboembolism: the Padua Prediction Score. J Thromb Haemost 2010;8:2450–7. https://doi.org/10.1111/j.1538-7836.2010.04044.x (PADUA)
- Blondon M, Righini M, Nendaz M, Glauser F, Robert-Ebadi H, Prandoni P, et al. External validation of the simplified Geneva risk assessment model for hospital-associated venous thromboembolism in the Padua cohort. J Thromb Haemost 2020;18:676–80. https://doi.org/10.1111/jth.14688 (GENEVA)
- Caprini JA, Arcelus JI, Hasty JH, Tamhane AC, Fabrega F. Clinical assessment of venous thromboembolic risk in surgical patients. Semin Thromb Hemost 1991;17 Suppl 3:304–12. (CAPRINI)
- Chopard P, Spirk D, Bounameaux H. Identifying acutely ill medical patients requiring thromboprophylaxis. J Thromb Haemost 2006;4:915–6. https://doi.org/10.1111/j.1538-7836.2006.01818.x (IMPROVE PREDICTIVE)
- Kucher N, Koo S, Quiroz R, Cooper JM, Paterno MD, Soukonnikov B, et al. Electronic alerts to prevent venous thromboembolism among hospitalized patients. N Engl J Med 2005;352:969–77. https://doi.org/10.1056/NEJMoa041533 (KUCHER)
- Spyropoulos AC, Anderson FA, Jr., FitzGerald G, Decousus H, Pini M, Chong BH, et al. Predictive and associative models to identify hospitalized medical patients at risk for VTE. Chest 2011;140:706–14. https://doi.org/10.1378/chest.10-1944 (IMPROVE ASSOCIATIVE)
- National Institute for Health and Care Excellence. Venous thromboembolism in over 16s: reducing the risk of hospital-acquired deep vein thrombosis or pulmonary embolism (NG89). nice.org.uk: NICE; 2018. URL: www.nice.org.uk/guidance/ng89 (accessed 22 July 2022). (DOH SCORE)
We found no cases where an alternate RAM recommended thromboprophylaxis but the Department of Health tool did not, in keeping with the low threshold (≥ 1 risk factor) of the latter tool. Results for each alternate RAM compared against the Department of Health tool are shown in Appendix 3, Figure 30.
Estimation of other key parameters for future implementation research
Baseline demographics and relevant clinical outcomes for the final cohort of patients are shown in Table 19, by speciality. In this cohort, surgical patients appeared to be younger with lower rates of medical comorbidity and lower rates of significant mobility reduction than medical patients.
| All patients(n = 254) | Medical admissions(n = 143) | Surgical admissions(n = 111) | |
|---|---|---|---|
| Demographics | |||
| Age | 62.6 (22.3)a | 70.4 (20.7)a | 52.7 (20.3)a |
| Female sex | 139 (54.7) | 82 (57.3) | 57 (51.4) |
| BMI | 25.7 (6.5)a | 24.4 (7.0)a | 27.3 (5.5)a |
| Active cancer | 15 (5.9) | 9 (6.3) | 6 (5.4) |
| Significant medical comorbidity | 192 (75.6) | 127 (88.8) | 65 (58.6) |
| Personal history of VTE | 16 (6.3) | 8 (5.6) | 8 (7.2) |
| Family history of VTE | 0 (0) | 0 (0) | 0 (0) |
| Significantly reduced mobility for > 3 days | 74 (29.1) | 55 (38.5) | 19 (17.1) |
| Active bleeding on admission | 12 (4.7) | 1 (0.7) | 11 (9.9) |
| Emergency admission | 251 (98.8) | 143 (100) | 108 (97.3) |
| Clinical outcomes | |||
| Pharmacological prophylaxis | 219 (86.2) | 115 (80.4) | 104 (93.7) |
| Hospital length of stay | 5 (2–11)b | 6 (1–16)b | 4 (2–6)b |
| Hospital-acquired thrombosis | 6 | 4 | 2 |
| (HAT) event rate | (2.4, 1.1–5.1) | (2.8, 1.1–7.0) | (1.8, 0.5–6.3) |
| PE | 1 (0.4) | 0 (0) | 1 (0.9) |
| Proximal DVT | 4 (1.6) | 3 (2.1) | 1 (0.9) |
| Distal DVT | 1 (0.4) | 1 (0.7) | 0 (0) |
| Major bleeding event rate | 3 | 1 | 2 |
| (1.2, 0.4–3.4) | (0.7, 0.1–3.9) | (1.8, 0.5–6.3) | |
| Fall in Hb > 2 g/dl | 1 (0.4) | 0 (0) | 1 (0.9) |
| Critical site | 1 (0.4) | 1 (0.7) | 0 (0) |
| Requiring surgical intervention | 1 (0.4) | 0 (0) | 1 (0.9) |
| Clinically relevant non-major bleeding event rate | 36 | 18 | 18 |
| (14.2, 10.4–19.0) | (12.6, 8.1–19.0) | (16.2, 10.5–24.2) | |
| Requiring medical intervention by a professional | 3 (1.2) | 1 (0.7) | 2 (1.8) |
| Leading to hospitalisation or increased level of care | 5 (2.0) | 2 (1.4) | 3 (2.7) |
| Prompting face-to-face assessment | 28 (11.0) | 15 (10.5) | 13 (11.7) |
| Alive at hospital discharge | 244 (96.5) | 135 (94.4) | 109 (98.2) |
We found six cases of HAT within this cohort, with a subsequent overall event rate of 2.4% (95% CI 1.1% to 5.1%). All six of the HAT cases in this cohort were diagnosed in patients who were prescribed thromboprophylaxis during hospital admission in line with an initial assessment using the Department of Health tool. It is, therefore, impossible to estimate whether application of other RAMs would have affected relevant clinical outcomes; all patients were receiving thromboprophylaxis.
Summary and key findings
- The majority of sites approached within this substudy had no mechanism for accessing individual variables recorded at VTE risk assessment, other than case note review of written notes. Only one site recorded risk variables electronically. This raises significant challenges for future implementation research looking to compare RAMs using routine data or through retrospective methodology.
- In this single-centre cohort of predominately emergency admissions, the proportion of patients receiving thromboprophylaxis in current practice was 86.2% (95% CI 81.2% to 90.1%). Over 90% of surgical inpatients received prophylaxis in the context of routine care.
- Prior to evaluation of bleeding risk, existing RAMs produce highly variable rates of recommended pharmacological prophylaxis when applied to the same cohort of medical and surgical hospital inpatients, ranging from 13% (95% CI 9.1% to 17.2%) to 91% (95% CI 86.3% to 93.6%).
- The national UK Department of Health VTE risk assessment tool conferred higher rates of potential prescribing in this cohort than any other RAM, by > 25%.