An assessment of the potential miscalibration of cardiovascular disease risk predictions caused by a secular trend in cardiovascular disease in England

A time-adjusted control chart for monitoring surgical outcome variations

BACKGROUND

Statistical Process Control (SPC) tools providing feedback to surgical teams can improve patient outcomes over time. However, the quality of routinely available hospital data used to build these tools does not permit full capture of the influence of patient case-mix. We aimed to demonstrate the value of considering time-related variables in addition to patient case-mix for detection of special cause variations when monitoring surgical outcomes with control charts.

METHODS

A retrospective analysis from the French nationwide hospital database of 151,588 patients aged 18 and older admitted for colorectal surgery between January 1st, 2014, and December 31st, 2018. GEE multilevel logistic regression models were fitted from the training dataset to predict surgical outcomes (in-patient mortality, intensive care stay and reoperation within 30-day of procedure) and applied on the testing dataset to build control charts. Surgical outcomes were adjusted on patient case-mix only for the classical chart, and additionally on secular (yearly) and seasonal (quarterly) trends for the enhanced control chart. The detection of special cause variations was compared between those charts using the Cohen's Kappa agreement statistic, as well as sensitivity and positive predictive value with the enhanced chart as the reference.

RESULTS

Within the 5-years monitoring period, 18.9% (28/148) of hospitals detected at least one special cause variation using the classical chart and 19.6% (29/148) using the enhanced chart. 59 special cause variations were detected overall, among which 19 (32.2%) discordances were observed between classical and enhanced charts. The observed Kappa agreement between those charts was 0.89 (95% Confidence Interval [95% CI], 0.78 to 1.00) for detecting mortality variations, 0.83 (95% CI, 0.70 to 0.96) for intensive care stay and 0.67 (95% CI, 0.46 to 0.87) for reoperation. Depending on surgical outcomes, the sensitivity of classical versus enhanced charts in detecting special causes variations ranged from 0.75 to 0.89 and the positive predictive value from 0.60 to 0.89.

CONCLUSION

Seasonal and secular trends can be controlled as potential confounders to improve signal detection in surgical outcomes monitoring over time.

Exploring Clinical Trajectories and the Continuum of Care for Patients With Acute Coronary Syndrome in the United Kingdom: A Thorough Cross-Sectional Analysis

The United Kingdom (UK) has a sustainable healthcare system. Nonetheless, the burden of acute coronary syndrome (ACS) is still a significant challenge. A scarcity of literature primarily focuses on the continuum of care for ACS patients in the UK. Moreover, limited research studies highlight the clinical trajectories of ACS patients across the UK. Therefore, the current study was designed to explore clinical trajectories and the continuum of care for patients with ACS in the UK. Secondary data was obtained from the Myocardial Ischaemia National Audit Project (MINAP) database. The latest data available in the MINAP database was used. As our objective was to explore clinical trajectories and the continuum of care for patients, we retrieved data regarding the care received by ACS patients admitted to hospitals across the UK.  The data of 85574 ACS patients was retrieved. A large number (n=47035) of patients were estimated to be eligible for the angiogram; however, an angiogram was performed for 87.15% (n=40995) of eligible patients. Angioplasty within 72 hours of admission was required for most (n=26313) ACS patients. Nonetheless, angioplasty within 72 hours of admission was performed for 59.7% (n=15703) of the eligible patients. There was a significant difference (P<0.05) between different regions of the UK and the percentage of patients for whom angioplasty was performed within 72 hours of admission. Primary percutaneous coronary intervention (PCI) was performed for 23923 ACS patients, of which the door-to-balloon interval for 17590 (73.5%) patients was ≤60 minutes while the door-to-balloon interval for 3086 (12.9%) patients was ≤90 minutes. Out of the total 85574 ACS patients, 65959 (77.08%) patients were discharged on appropriate medications, while 19615 (22.92%) were transferred to another hospital or died there. A total of 75361 were eligible to be referred to cardiac rehabilitation settings. Nonetheless, 64518 (85.61%) were referred to cardiac rehabilitation. About 85000 patients were reported in the UK (England, Northern Ireland, Wales). Optimal care was provided to most patients in the UK. However, some patients received sub-optimal care, highlighting the disparity in the healthcare system. There is a need to explore further the factors that might be responsible for the sub-optimal care to the patients.

Machine learning-based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries

Many countries curate national registries of liver transplant (LT) data. These registries are often used to generate predictive models; however, potential performance and transferability of these models remain unclear. We used data from 3 national registries and developed machine learning algorithm (MLA)-based models to predict 90-day post-LT mortality within and across countries. Predictive performance and external validity of each model were assessed. Prospectively collected data of adult patients (aged ≥18 years) who underwent primary LTs between January 2008 and December 2018 from the Canadian Organ Replacement Registry (Canada), National Health Service Blood and Transplantation (United Kingdom), and United Network for Organ Sharing (United States) were used to develop MLA models to predict 90-day post-LT mortality. Models were developed using each registry individually (based on variables inherent to the individual databases) and using all 3 registries combined (variables in common between the registries [harmonized]). The model performance was evaluated using area under the receiver operating characteristic (AUROC) curve. The number of patients included was as follows: Canada, n = 1214; the United Kingdom, n = 5287; and the United States, n = 59,558. The best performing MLA-based model was ridge regression across both individual registries and harmonized data sets. Model performance diminished from individualized to the harmonized registries, especially in Canada (individualized ridge: AUROC, 0.74; range, 0.73-0.74; harmonized: AUROC, 0.68; range, 0.50-0.73) and US (individualized ridge: AUROC, 0.71; range, 0.70-0.71; harmonized: AUROC, 0.66; range, 0.66-0.66) data sets. External model performance across countries was poor overall. MLA-based models yield a fair discriminatory potential when used within individual databases. However, the external validity of these models is poor when applied across countries. Standardization of registry-based variables could facilitate the added value of MLA-based models in informing decision making in future LTs.

Prognostic model for atrial fibrillation after cardiac surgery: a UK cohort study

OBJECTIVE

To develop a validated clinical prognostic model to determine the risk of atrial fibrillation after cardiac surgery as part of the PARADISE project (NIHR131227).

METHODS

Prospective cohort study with linked electronic health records from a cohort of 5.6 million people in the United Kingdom Clinical Practice Research Datalink from 1998 to 2016. For model development, we considered a priori candidate predictors including demographics, medical history, medications, and clinical biomarkers. We evaluated associations between covariates and the AF incidence at the end of follow-up using logistic regression with the least absolute shrinkage and selection operator. The model was validated internally with the bootstrap method; subsequent performance was examined by discrimination quantified with the c-statistic and calibration assessed by calibration plots. The study follows TRIPOD guidelines.

RESULTS

Between 1998 and 2016, 33,464 patients received cardiac surgery among the 5,601,803 eligible individuals. The final model included 13-predictors at baseline: age, year of index surgery, elevated CHA₂DS₂-VASc score, congestive heart failure, hypertension, acute coronary syndromes, mitral valve disease, ventricular tachycardia, valve surgery, receiving two combined procedures (e.g., valve replacement + coronary artery bypass grafting), or three combined procedures in the index procedure, statin use, and ethnicity other than white or black (statins and ethnicity were protective). This model had an optimism-corrected C-statistic of 0.68 both for the derivation and validation cohort. Calibration was good.

CONCLUSIONS

We developed a model to identify a group of individuals at high risk of AF and adverse outcomes who could benefit from long-term arrhythmia monitoring, risk factor management, rhythm control and/or thromboprophylaxis.