Monitoring the quality of cardiac surgery based on three or more surgical outcomes using a new variable life-adjusted display

Modeling the patient mix for risk-adjusted CUmulative SUM charts

The improvement of surgical quality and the corresponding early detection of its changes is of increasing importance. To this end, sequential monitoring procedures such as the risk-adjusted CUmulative SUM chart are frequently applied. The patient risk score population (patient mix), which considers the patients’ perioperative risk, is a core component for this type of quality control chart. Consequently, it is important to be able to adapt different shapes of patient mixes and determine their impact on the monitoring scheme. This article proposes a framework for modeling the patient mix by a discrete beta-binomial and a continuous beta distribution for risk-adjusted CUSUM charts. Since the model-based approach is not limited by data availability, any patient mix can be analyzed. We examine the effects on the control chart’s false alarm behavior for more than 100,000 different scenarios for a cardiac surgery data set. Our study finds a negative relationship between the average risk score and the number of false alarms. The results indicate that a changing patient mix has a considerable impact and, in some cases, almost doubles the number of expected false alarms.

Integrating continuous monitoring and evaluation of risk-adjusted outcomes in a cardiac surgical program

BACKGROUND

The variable life-adjusted display (VLAD) method shows the difference between predicted and observed outcomes over time. Our study aims to implement routine in-house monitoring of risk-adjusted 30-day mortality and morbidity following cardiac surgery.

METHODS

The Society of Thoracic Surgeons (STS) risk score was calculated for 249 isolated and combined coronary and aortic or mitral valve cases performed during a 6-month period. The nine predicted STS variables were operative mortality, permanent stroke, renal failure (RF), prolonged ventilation, deep sternal wound (DSW) infection, reoperation for any reason, short and long length of stay (LOS), and major morbidity or operative mortality. EuroSCORE II was also calculated for the study population. VLAD plots were generated for each variable indicating whether performance is better or worse than expected on the basis of predicted risk of failure.

RESULTS

The mortality plot was fluctuating close to baseline risk. The prolonged ventilation, RF, reoperation, morbidity/mortality, and LOS plots were consistently positive, indicating favorable results. The stroke chart showed an upward trend for most of the period until two incidents toward last month led to a steep descent. The DSW infections plot though, indicated a worse-than-expected performance. The VLAD charts were shared in multidisciplinary meetings and clinicians were able to confront the performance with the population-specific expectancies and respond to adverse trends with further actions.

CONCLUSION

Graphical tool monitoring of risk-adjusted 30-day mortality and morbidity following cardiac surgery is feasible and allows detection of underperformance and implementation of changes in clinical practice.

Days at Home after Surgery: An Integrated and Efficient Outcome Measure for Clinical Trials and Quality Assurance

BACKGROUND

Surgical audit, sometimes including public reporting, is an important foundation of high quality health care. We aimed to assess the validity of a novel outcome metric, days at home up to 30 days after surgery, as a surgical outcome measure in clinical trials and quality assurance.

METHODS

This was a multicentre, registry-based cohort study. We used prospectively collected hospital and national healthcare registry data obtained from patients aged 18 years or older undergoing a broad range of surgeries in Sweden over a 10-year period. The association between days at home up to 30 days after surgery and patient (older age, poorer physical status, comorbidity) and surgical (elective or non-elective, complexity, duration) risk factors, process of care outcomes (re-admissions, discharge destination), clinical outcomes (major complications, 30-day mortality) and death up to 1 year after surgery were measured.

FINDINGS

From January, 2005, to December, 2014, we obtained demographic and perioperative data on 636,885 patients from 21 Swedish hospitals. Mortality at 30 days and one year was 1.8% and 7.3%, respectively. The median (IQR) days at home up to 30 days after surgery was 27 (23-29), being significantly lower among high-risk patients, those recovering from more complex surgical procedures, and suffering serious postoperative complications (all p < 0.0001). Patients with 8 days or less at home up to 30 days after surgery had a nearly 7-fold higher risk of death up to 1 year postoperatively when compared with those with 29 or 30 days at home (adjusted HR 6.78 [95% CI: 6.44-7.13]).

INTERPRETATION

Days at home up to 30 days after surgery is a valid, easy to measure patient-centred outcome metric. It is highly sensitive to changes in surgical risk and impact of complications, and has prognostic importance; it is therefore a valuable endpoint for perioperative clinical trials and quality assurance.

FUNDING

Swedish National Research Council Medicine and Stockholm County Council ALF-project grant (LE), and the Australian National Health and Medical Research Council (PM).

Monitoring data quality for telehealth systems in the presence of missing data

BACKGROUND

All-in-one station-based health monitoring devices are implemented in elder homes in Hong Kong to support the monitoring of vital signs of the elderly. During a pilot study, it was discovered that the systolic blood pressure was incorrectly measured during multiple weeks. A real-time solution was needed to identify future data quality issues as soon as possible.

METHODS

Control charts are an effective tool for real-time monitoring and signaling issues (changes) in data. In this study, as in other healthcare applications, many observations are missing. Few methods are available for monitoring data with missing observations. A data quality monitoring method is developed to signal issues with the accuracy of the collected data quickly. This method has the ability to deal with missing observations. A Hotelling's T-squared control chart is selected as the basis for our proposed method.

FINDINGS

The proposed method is retrospectively validated on a case study with a known measurement error in the systolic blood pressure measurements. The method is able to adequately detect this data quality problem. The proposed method was integrated into a personalized telehealth monitoring system and prospectively implemented in a second case study. It was found that the proposed scheme supports the control of data quality.

CONCLUSIONS

Data quality is an important issue and control charts are useful for real-time monitoring of data quality. However, these charts must be adjusted to account for missing data that often occur in healthcare context.