A dual domain systematic review and meta-analysis of risk tool accuracy to predict cardiovascular morbidity in prehypertension and diabetic morbidity in prediabetes

Abstract

Objective:

Health forecasting predicts population trends through risk prediction algorithms which can estimate the risk of future disease developing. Screening algorithms can systematically identify patients with a high probability of undiagnosed diseases for diagnostic testing. We describe a dual domain systematic review and meta-analysis of the accuracy of available risk tools to (1) predict prehypertensive deterioration to cardiovascular morbidity, & (2) predict prediabetes deterioration to diabetic morbidity.

Materials and Methods:

The primary outcome was the accuracy of the risk scores, and the secondary outcomes were the reporting quality and risk of bias. The dual domain systematic review included studies involving risk tools for (1) prehypertensive adults to predict cardiovascular morbidity (including hypertension, stroke and coronary heart disease) and (2) prediabetic adults to predict diabetic morbidity (including Type 2 Diabetes and end organ damage, such as diabetic nephropathy). Following PROSPERO registration (IDs 425686 & 425683), searches were conducted in PubMed, MEDLINE and Google Scholar.

Results:

Accuracy of risk prediction in prehypertension and prediabetes was high: the pooled C statistic for All Cause Cardiovascular Disease was 0.77 (CI 0.71, 0.84) and the pooled Sensitivity for All Cause Diabetic Disease Spectrum risk was 0.68 (CI 0.65, 0.7). However, we found high risk of bias, with inconsistent reporting in both prehypertension and prediabetes papers.

Discussion:

We propose nine recommendations for policymakers and commissioners, organised under an “A to I” framework.

Conclusion:

We found that predictive performance was generally accurate. However, there remain limitations due to methodological inconsistency, such as timeframe, which undermines comparison.

1 Introduction

The chronic disease burden on health systems is a global challenge. Half of the US population has a chronic disease, and 86% of health costs are attributable to chronic disease (1). Health systems are struggling to plan resource distribution to respond. There are two components of the necessary solution, health forecasting and predisease screening. Health forecasting predicts trends in future health events at a population level. This is achieved through risk prediction algorithms which can estimate the risk of future disease developing. Screening algorithms can systematically identify patients with a high probability of undiagnosed diseases for diagnostic testing. Predisease is of particular interest as a precursor of chronic morbidity.

Accurate health forecasts enable improvements in preventive health services, generate patient flow alerts and reduce staff costs. Prehypertension is defined as a systolic blood pressure of 120-139mmHg, and a diastolic blood pressure 80-89mmHg (2), and is a precursor to cardiovascular disease, such as stroke and myocardial infarction. Moreover, in the UK, one in four adults suffer from high blood pressure, it is the third most common reason for premature death, at least half of heart attacks and strokes are associated with hypertension, and it can lead to chronic organ failure and premature death (3). Prediabetes is a non-diabetic hyperglycaemic state (4) which enables warning of the development of diabetic disease; in the UK, around 7 million people are estimated to have prediabetes and therefore have a high risk for developing type 2 diabetes (5). Understanding potential trajectories in health directs long-term investments and policy implementation. This warning of chronic disease makes prediabetes and prehypertension amongst the most impactful targets of risk model products.

Past work on forecasting has provided an incomplete landscape of future health scenarios, highlighting the need for a more robust modelling platform to inform policy (6). In-home care which delivers intervention preemptively may reduce costs associated with non-urgent hospital care (7), and thus allow health forecasting to inform the allocation of resources. Through embedding risk scores into digital health tools, prediction capabilities can help patient self-care and doctor management plans. An electronic personal health record is one type of technology commonly used to support diabetes self-management (8). Preemptive analysis of electronic health records (EHRs) is vital for patient safety. The use of digital health tools could save approximately $7 billion a year in U.S. healthcare spending, equivalent to 1.4% of total expenditures (9). If artificial intelligence can assist in the accurate identification of groups in a population most at risk of developing chronic disease, resource allocation will be more effective. In the UK, ‘Core20Plus5’ (10) is an initiative to reduce healthcare inequalities, in which a target outpatient population of the most deprived 20% of society and five key diagnostic priorities (including hypertension and lipid management) are prioritised, simultaneously saving resources and improving health engagement.

The deterioration of model performance due to drift and bias present two major governance challenges to global health policy leaders. Whilst artificial intelligence may assist in addressing the priorities of ‘Core20Plus5’, there are risks that alternative inequalities may be exacerbated by model bias. For example, hypertension disproportionately affects Afro-Caribbean ethnicities (11); in 2019, an algorithm built using historical data reportedly produced healthcare predictions that favoured white people above black people in the US (12). Nevertheless, this project is directly in line with the official objectives of the Commonwealth Fund, the WHO and UK National Health Policy, and will support the UK’s digital transformation (13); it will act on the ES(H)G investment principles set out in the Business for Health initiative (14) and supports the ambitions of Our Future Health (15). Herein, we describe a dual domain systematic review and meta-analysis of the accuracy of available risk tools to predict prehypertensive deterioration to cardiovascular morbidity & prediabetes deterioration to diabetic morbidity.

2 Methods

This dual domain systematic review and meta-analysis was conducted according to a registered protocol and is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (16).

2.1 Information sources and search strategy

Following the PROSPERO registration (IDs 425686 & 425683), a systematic literature search was performed across multiple databases to identify relevant studies for reviews on Prediabetes and Prehypertension. Initial searches were conducted in PubMed, MEDLINE, and Google Scholar. Secondary searches in EMBASE, The Cochrane Library, Health Technology Assessment Database, and Web of Science yielded only duplicate records, which were removed during the deduplication process in Covidence. Covidence was also used for abstract screening and to manage references throughout the review process. The search strategy was structured with both keyword and MeSH terms to ensure comprehensive coverage of relevant literature. The full systematic search included all publications available up to 10/05/2023. For each review, we specified MeSH terms alongside keywords to target specific populations, conditions, and risk assessments:

Prediabetes Review

Keywords: “diabetes AND risk tool AND prediabetes” OR “diabetes AND risk score AND prediabetes.”

MeSH Terms:

“Diabetes Mellitus, Type 2”

“Prediabetic State”

“Risk Assessment”

“Risk Factors”

Prehypertension Review

Keywords: “risk tool AND prehypertension AND cardiovascular disease” OR “risk score AND prehypertension AND cardiovascular disease.”

MeSH Terms:

“Hypertension”

“Prehypertension”

“Cardiovascular Diseases”

“Risk Assessment”

“Risk Factors”

Search Parameters and Filters

Boolean operators (AND, OR) were employed to refine and combine search terms effectively. Searches were limited to studies published in English and involving human participants, with no restrictions on publication date. In the final stage, advice was sought from the library services at Imperial College London to further refine the search protocol.

Data Management

Search results from all databases were uploaded into Covidence, which was used to remove duplicates, manage citations, and streamline the abstract screening process.

2.2 Eligibility criteria

The exclusion criteria were if the article was not in English, and not about the (1) prehypertension to hypertension, or (2) prediabetes to diabetes disease spectrum respectively, not reporting accuracy data, not a prediction tool, the subjects included children aged (0-17), or a meta-analysis, Editorial/Opinion Article.

2.3 Selection process

The selection process was performed in three stages: first, titles were screened for relevance. Second, abstracts of the selected titles were reviewed. Finally, full-text articles were assessed for eligibility. Duplicates were removed using Covidence software, and all stages were performed independently by two reviewers (WW & NT). Any discrepancies were resolved by consulting a third reviewer (HA).

2.4 Risk of bias

Two review authors (WW & NT) independently screen assessed the risk of bias with the prediction model risk of bias assessment tool PROBAST, which is organised into the following 4 domains: participants, predictors, outcome, and analysis (17). This explores how weaknesses in study design, conduct, or analysis can lead to systematically distorted estimates of model predictive performance (17). Any discrepancy involved a third senior supervisor colleague (HA) being consulted.

2.5 Data extraction

Two independent review authors assisted in the stat extraction and subsequent meta-analysis. It was recorded in a mutually shared Excel file with two researchers checking the results. Any discrepancy involved a third colleague being consulted. Individual studies which met the inclusion criteria were included in the statistical analysis, with checks included to ensure no duplication of results under analysis. In the event of an apparent duplication, analysis only included new data from additional studies not already represented.

2.6 Data synthesis

These search strategies were kept separate. The dual domain systematic review to concomitantly appraise two risk tools included studies involving risk tools for (1) prehypertensive adults to predict cardiovascular morbidity (including hypertension, stroke and coronary heart disease) and (2) prediabetic adults to predict diabetic morbidity (including Type 2 Diabetes and end organ damage, such as diabetic nephropathy). The dual domain systematic review was conducted in Covidence with data extracted for analysis according to the following categories: study, author, year, population, risk score, disease, time period and accuracy. It was recorded in a mutually shared Excel file with two researchers (WW & NT) checking the results.

Risk ratios for individual studies were combined using a random-effects meta-analysis, which presents the extent of between-study variation and enables Chi², I² & Tau² heterogeneity analysis. Only studies predicting cardiovascular disease or diabetic disease, respectively, over a fixed time period were considered. The different risk tools and their respective performance in predicting cardiovascular & diabetic morbidity were analysed as subgroups. The software used to conduct the meta-analysis was StataCorp. ((2017). Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC). We provided a narrative synthesis of the study findings and meta-analysis of the accuracy of the two domains of predisease risk tools.

3 Results

3.1 Study selection

3.1.1 Prehypertension

The prehypertension search identified 1793 relevant citations. After removing duplicate results, 1555 articles were screened for titles and abstracts, and 44 studies were included for full-text review. 27 articles were excluded after full-length review due to lack of predictive clarity as per the PROBAST criteria. Thus,17 studies were eligible for inclusion in the study (Figure 1a), with a total of 3,077,131 patients represented in the final meta-analysis, after accounting for the risk of double counting patients in different studies. The number of patients involved in each study ranged from 302 to 1,129,098, and the descriptive variables are displayed in Table 1a. Table 2a provides a Summary of Results. Figure 2a describes the PROBAST (17) Risk of Bias assessment. Figures 3 and 4 describe the subgroups of results.

Figure 1

Figure 2

Figure 3

Figure 4

3.1.2 All cause cardiovascular disease

The pooled C statistic for All Cause Cardiovascular Disease was 0.77 (CI 0.71, 0.84) across a population of 42,631. When assessing the C-statistic for prediction scores of the development of all cause cardiovascular disease, the Chi² heterogeneity was 8.9e+07, the I² variation attributable to heterogeneity was 100% and the Tau² between-study variance was 0.0055.

The pooled Hazard Ratio for All Cause Cardiovascular Disease was 1.55 CI 1.38, 1.71) across a population of 22,512. When assessing the Hazard Ratio for all cause cardiovascular disease, the Chi² heterogeneity was 1.73, and the I² variation attributable to heterogeneity was 0.

The pooled Risk Ratio for All Cause Cardiovascular Disease was 1.29 (CI 1.26, 1.32) across a population of 2,824,371 (Figure 3) and was found to have a Chi² heterogeneity 98.45 and I² variation attributable to heterogeneity was 93.9%.

3.1.3 Hypertension

The pooled C Statistic for Hypertension was 0.77 (CI 0.77, 0.78) across a population of 20,039. When assessing the C-Statistics of prediction scores of the development of hypertension, the Chi² heterogeneity was 5.2e+06, the I² variation attributable to heterogeneity was 100% and the Tau² estimate of between-study variance was 0.0001.

3.1.4 Stroke

The pooled Risk Ratio for Stroke was 1.67 (CI 1.59, 1.76) across a population of 852,402 (Figure 4). When the Stroke Risk Ratio was assessed, the Chi² heterogeneity was found to be 0.25 and the I² variation attributable to heterogeneity was 0%.

3.2 Prehypertension risk of bias

Amongst the 29 study subgroups which underwent PROBAST (17) ‘risk of bias’ evaluation (Figure 2a), 86% (25/29) study subgroups were found to have some concerns of bias and 14% (4/29) studies were found to have low bias. In the subdomain analysis, concerns of bias were found to be 41% (12/29) in the Participants section, 10% (3/29) in the Predictors section, 28% (8/29) in the Outcome section and 17% (5/29) in the Analysis section. Any discrepancy involved a senior third colleague being consulted. Individual studies which met the inclusion criteria were included in the statistical analysis, with checks included to ensure no duplication of results under analysis.

3.2.1 Prediabetes

The prediabetes search identified 1500 relevant citations. After removing duplicate results, 1345 articles were screened for titles and abstracts, and 116 studies were included for full-text review. 65 articles were excluded after full-length review due to lack of predictive clarity as per the PROBAST criteria. Thus, 51 studies were eligible for inclusion in the study (Figure 1b), with a total of 2,193,555 patients represented in the final meta-analysis, after accounting for the risk of double counting patients in different studies. The descriptive variables are displayed in Table 1b. Table 2b provides a Summary of Results. Figure 2b describes the PROBAST (17) Risk of Bias assessment. Figures 5 and 6 describe the subgroups of results.

Figure 5

Figure 6

3.3 All cause diabetic disease spectrum

- Sensitivity

When assessing All Cause Diabetic Disease Spectrum risk, the pooled Sensitivity was 0.68 (CI 0.65, 0.7), with a Chi² heterogeneity 1.6e+09, an I² variation attributable to heterogeneity 100% and a Tau² estimate of between-study variance of 0.0156.

- Specificity

When assessing All Cause Diabetic Disease Spectrum risk, the pooled Specificity was 0.66 (CI 0.64, 0.67), with a Chi² heterogeneity 2.2e+09, an I² variation attributable to heterogeneity 100% and a Tau² estimate of between-study variance of 0.0267.

- Positive Predictive Value

When assessing All Cause Diabetic Disease Spectrum risk, the pooled Positive Predictive Value was 0.27 (CI 0.24, 0.30), with a Chi² heterogeneity 2.2e+09, an I² variation attributable to heterogeneity 100% and a Tau² estimate of between-study variance of 0.0193.

Pre Diabetes

Figure 5 describe the meta-analysis for Prediabetes risk, representing 497,240 patients in total.

- Sensitivity

When assessing Prediabetes risk, the pooled Sensitivity was 0.56 (CI 0.48, 0.63) (Figure 5a), with a Chi² heterogeneity 1.2e+08, an I² variation attributable to heterogeneity 100% and a Tau² estimate of between-study variance of 0.0248.

- Specificity

When assessing Prediabetes risk, the pooled Specificity was 0.70 (CI 0.63, 0.77) (Figure 5b), with a Chi² heterogeneity 1.3e+08, an I² variation attributable to heterogeneity 100% and a Tau² estimate of between-study variance of 0.0215.

- Positive Predictive Value

When assessing Prediabetes risk, the pooled Positive Predictive Value was 0.39 (CI 0.32, 0.45) (Figure 5c), with a Chi² heterogeneity 9.7e+06, an I² variation attributable to heterogeneity 100% and a Tau² estimate of between-study variance of 0.0143.

3.3.1 Diabetes

Figure 6 describe the meta-analysis for Diabetes risk, representing 1,696,315 patients in total.

- Sensitivity

When assessing Diabetes risk, the pooled Sensitivity was 0.69 (CI 0.67, 0.71) (Figure 6a), with a Chi² heterogeneity 6.2e+08, an I² variation attributable to heterogeneity 100% and a Tau² estimate of between-study variance of 0.0136.

- Specificity

When assessing Diabetes risk, the pooled Specificity was 0.66 (CI 0.62, 0.70) (Figure 6b), with a Chi² heterogeneity 1.7e+09, an I² variation attributable to heterogeneity 100% and a Tau² estimate of between-study variance of 0.0540.

- Positive Predictive Value

When assessing Diabetes risk, the pooled Positive Predictive Value was 0.25 (CI 0.22, 0.28) (Figure 6c), with a Chi² heterogeneity 2.1e+09, an I I² variation attributable to heterogeneity 100% and a Tau² estimate of between-study variance of 0.0192.

3.4 Prediabetes risk of bias

Amongst the 50 study subgroups which underwent PROBAST (17) ‘risk of bias’ evaluation (Figure 2b), 80% (40/50) studies were found to have some concerns of bias and 20% (10/50) studies were found to have low bias. In the subdomain analysis, concerns of bias were found to be 52% (26/50) in the Participants section, 4% (2/50) in the Predictors section, 58% (29/50) in the Outcome section and 4% (2/50) in the Analysis section. Any discrepancy involved a senior third colleague being consulted. Individual studies which met the inclusion criteria were included in the statistical analysis, with checks included to ensure no duplication of results under analysis.

4 Discussion

We performed a dual domain systematic review to evaluate the accuracy of risk tools to predict cardiovascular morbidity in prehypertension & diabetic morbidity in prediabetes. We found that predictive performance was generally accurate. However, there remain limitations due to confounders and methodological inconsistency, such as timeframe, which undermines comparison. We found that the pooled C statistic for All Cause Cardiovascular Disease was 0.77 (CI 0.71, 0.84) and the Hazard Ratio for All Cause Cardiovascular Disease was 1.55 (CI 1.38, 1.71). When assessing All Cause Diabetic Disease Spectrum risk, the pooled Sensitivity was 0.68 (CI 0.65, 0.7) and the pooled Specificity was 0.66 (CI 0.64, 0.67).

Translation of risk modelling into health systems is challenged by population heterogeneity (85), and the reliability of reporting to enable valid comparison across specific time periods and specific endpoints. Without more consistent standards of data disclosure, academic and commercial communities may begin to polarise to serve private sector interests. However, this could be mitigated by the availability of multivariate, granular data which offers the possibility of a new ‘social contract’ (86) in which artificial intelligence serves digitally literate citizens who retain autonomy of their data. To mitigate against model drift, we need to be able to benchmark model performance using last measurement prediction (87) to facilitate comparisons of the performance from different pools of data. A ‘model-agnostic data-driven deep learning model’ (87) needs to be grounded in a physiological model to provide meaningful, explainable clinical insights. Of note is the success of the AUSDRISK tool for prediabetes screening in primary care, with a >17 score identifying 75% at risk (56). Risk scores provide valuable analysis to direct deployment of limited resources, but there are ongoing debates among health economists to define costs and deployment of preventative treatments (88). In a review of German Primary Care Diabetes and Cardiovascular Risk Scores, automated risk scores were most impactful alongside advanced information retrieval technologies (89), although patient engagement should be quantified as part of health risk in view of the role of self-management in multimorbid chronic disease (90). The optimal integration of machine learning would be the curation of the optimal variables in different populations’ risk score. This would pave the way for bespoke forecasting in ever more precise patient cohorts, with incorporation into established genetic forecasting services.

However, algorithmic fairness is an essential consideration to ensure population risk prediction tools do not exacerbate inequalities. Demographic bias is an important consideration when evaluating risks to the fairness of an algorithm. High heterogeneity and variance between studies undermines the certainty around estimates of diagnostic accuracy. The extensiveness of the heterogeneity precludes directive interpretation from the results of this analysis. Predictive models may improve over time with increased exposure to data, although the literature currently has a trend towards high-income nations, undermining the translation of applications to ‘global south’ nations who may exhibit different disease burdens and health behaviours. There are ongoing ethical concerns in the predictive modelling community regarding diversity and economics (7). It is ethically unacceptable for risk models to only serve the interests of a privileged minority of the global population.

The studies in this dual domain systematic review show substantial variation in accuracy metrics across both cardiovascular & diabetic morbidity, alongside inconsistent reporting preventing sensitivity and specificity comparisons across all studies. Most studies were challenged by inconsistent definitions of the spectrum of diabetic disease and reporting deficiencies. Confidence intervals were intermittently declared. Datasets with homogeneous groupings in specific populations, particular regions and blood glucose ranges, were especially accurate in forecasting prediabetes development. There was significant variation in the number of patients each score was assessed with, distorting the available valid comparison methods. The search strategy led us to scrutinise papers which ultimately, do not all offer what they presented. The inconsistency in predictive score performance, even the same score in different geographies, may be attributable to the context, comorbidities, diet, and recording of local patient characteristics. Predictive scores show promise in supporting clinical decision making but there is inconsistent evidence to inform regulation, best practice, and integration into ‘front line’ healthcare products.

The systematic and safe deployment of risk algorithms into clinical use requires attention paid to policy and governance, as well as technical aspects of data and deployment infrastructure. We propose nine recommendations for policymakers and commissioners, organised under an “A to I” framework.

A) Algorithmic (generalisability)

Predictive performance in these reviews was found to vary across key demographic population subgroups. The inherent differences in patient subpopulations and disease spectrum definitions threatens generalisability and subsequent plans for Personalised Electronic Health Record forecasting. Datasets with homogeneous groupings in specific populations will be especially accurate in forecasting predisease development. Ongoing challenges with heterogenous populations make local context deployment challenging. There are potential benefits to generalisability through the combination of foundation models and electronic health records: better predictive performance & sample efficiency, simple model deployment and effective engagement with multimodal data (91). However, foundation models are complex to deploy, and have unexplored safety challenges.

B) Bias

The impact of risk scores is inconsistent (92) due to bias in training data. Those patients at highest risk of developing diabetes in a time frame of five to ten years are identifiable by predictive scores (93), but the most effective method to improve disability free life expectancy and reduce complications related to metabolic disease will be through earlier intervention at the predisease end of the spectrum. This will not be realised without commercial and academic collaboration in adherence to consistent reporting standards and representative data.

C) Change and quality

A serious challenge to risk scores is performance degradation: once a risk model is deployed, there are a diminishing number of ground truths in the present day for valid comparison, and outcome data that does get collected may be contaminated by the intervention, which presents challenges to retrain the model once drift ensues (94). The new UK federated data platform may enable secure, regional data analytics with greater flexibility for local services (95), however, the new Secure Data Environments may not widen information or population diversity (95).

D) Data source

Relative to fragmented data architecture, ‘data lakes’ (96) enable more reliable training of predictive scores and more consistent reporting patterns in global collaboration on preemptive medicine. Biomarkers are important in the risk stratification for early detection (97), with a notable success of risks scores including the Polygenic Risk Score to predict susceptibility to coronary heart disease and atrial fibrillation, enabling appropriate impact through intervention and lifestyle change (98).

E) Ethics

Evidence from real-world cases (99) needs to be compiled to ensure quality training optimises diagnostic and triage accuracy (100). Further development of transparency and diversity reporting standards, such as the ‘Health sheet’ initiative (101, 102), can help reduce established ethnic inequalities in AI datasets, as per STANDING Together (103). Economic concerns remain in conversations about the potential for insurance systems to discriminate against individuals and families based on their perceived risk profiles.

F) Functionality & ‘explainability’

The expression of disease risk across predisease spectra will be helpful to stratify patients based on their Personal Health Record data. For example, an artificial intelligence for prediction could perform using a scale for hypo- and hyper-glycemia risk, as opposed to arbitrary categories, reflecting the reality of the spectrum of disease (87). The risk profile must be grounded in physiological reality relative to potential deterioration to be useful; we need to be able to explain the disease spectrum to inform intervention.

G) Governance

Leaders with training in computer and medical science are needed to direct EHR predictive modelling technologies. This emphasis on risk scores is economically justified since cardiovascular disease (CVD) risk modelling has been projected to save £68 billion, gain 4.9 million QALYs and prevent 3.4 million CVD cases over 25 years in England (104). This leadership role will require the oversight of new guidelines like STARD-AI and CONSORT-AI (105, 106), to encompass EHR risk scores which use primary care demographics and prescription history, as already applied in Victoria, Australia (107).

H) Humans in the loop

‘Humans In The Loop’ (HITL) are a safety mechanism where experts will review and modify the decision-informing outputs of an algorithmic system. The NHS needs set apart Clinical Informaticians to supervise risk scores in EHRs against multimorbidity, one of the greatest challenges facing modern health services (108). This is especially urgent whilst the burden of CVD in the young is growing (109), and the polygenic risk score only marginally improves coronary heart disease forecasting in young adults (110). HITL clinical specialty pathways will help optimise the deployment of risk scores.

I) Interoperability

Any new risk score capability will need to integrate into legacy technology in health systems. This review found that the importance of subcomponents of a risk score differed according to the population. Set apart Clinical Informaticians are especially important to supervise the application of risk scores which otherwise systematically underestimate risk in particular ethnic, socioeconomic and chronic disease groups (111). False negatives are best mitigated with disease catalogues for underprivileged groups to improve the integration of risk score software into clinical practice (89).

5 Limitations

5.1 Prehypertension

Bias resulted from retrospective studies in which documentation, symptoms and follow up outcomes will vary across geographies. Variance in performance is hard to account for in a cross-sectional study, although there may be improvement in predictive reliability as input data grows in fidelity and volume to characterise forecasted prognosis more accurately. Analysis was undertaken on hypertension diagnosis, stroke, and all cause cardiovascular disease, however, the definitions of these events differed in reporting. The review itself was limited by the short search strategy, despite many duplications showing comprehensive coverage of the relevant material.

5.2 Prediabetes

Studies rarely engaged in external validation and often struggled to demonstrate that the target population was representative. Those scores focusing on prediabetes had a lack of transparency about the cut-off points for defining prediabetes and there was significant variation in the metrics of performance. The studies lacked a reliable method of demonstrating predictive accuracy and did not conduct reports transparently. The review itself was limited by the short search strategy, despite many duplications showing comprehensive coverage of the relevant material.

5 Conclusion

In this systematic review, cardiovascular & diabetic risk tool accuracy prediction varied due to reporting standards but was most valuable in all cause cardiovascular mortality as a useful warning system which could be deployed to an EHR national screening programme. The risk tools are consistent and valuable in predicting hypertensive risk, but there are ongoing concerns about unrepresentative training data. Artificial intelligence may have a role in the curation of variables to build the optimal algorithm for different populations, deployed as an Application Programming Interface in EHRs. However, governance decisions are challenging due to model drift and bias. Further work is needed to characterise the specific time points along the spectrum of cardiovascular & diabetic disease which signify acceleration in clinical deterioration, enabling accurate forecasting.

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