Statistical methods to monitor risk factors in a clinical database: example of a national cardiac surgery registry

Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

BACKGROUND

Recurrent mood episodes and subsyndromal mood instability cause substantial disability in patients with bipolar disorder. Early identification of mood episodes enabling timely mood stabilization is an important clinical goal. This study investigates the ability of control chart methodology to predict manic and/or depressive episodes by applying Shewhart's control rules to weekly self-reported scores from mania and depression questionnaires.

METHODS

Shewhart's control rules were applied to weekly self-reported scores from the Altman Self-Rating Mania Scale (ASRM) and the Quick Inventory of Depressive Symptomatology-Self-Report (QIDS) collected from 2001 to 2012 as part of the OXTEXT programme. Manic and depressive episodes were defined as an ASRM score ≥ 10 or a QIDS score ≥ 15, respectively. An episode-free run-in period of eight consecutive weeks without an episode of either type was used to calibrate control charts. Shewhart's rules were then applied to follow-up data. Their sensitivity and positive predictive value for predicting manic or depressive episodes within the next 4 weeks were calculated focusing on the first episode. Secondary analyses varying control chart type, length of episode-free run-in period, time frames to evaluate diagnostic accuracy, thresholds defining either manic or depressive episodes, and missing data methods were performed.

RESULTS

Data from 146 participants (37% men) were included. The mean age was 43.4 (SD = 13.3) years. The median follow-up was 10 (IQR 5-40) weeks for mania and 10 (IQR 5-23) weeks for depression. A total of 53 (36%) participants had a manic episode and 67 (46%) had a depressive episode. For manic episodes, the sensitivity and positive predictive value of Shewhart's control rules were 30% (95% CI 19-45%) and 7% (95% CI 5-9%), and for depressive episodes, 33% (95% CI 22-46%) and 9% (95% CI 6-12%), respectively. Results from secondary analyses were similar to these.

CONCLUSIONS

Tele-monitoring with control rules has the potential to predict about one-third of manic or depressive episodes before they occur, at the cost of a high false positive rate. Given the severe consequences of manic and depressive episodes, this trade-off may be desirable.

Controlled Exponentially Weighted Moving Average Chart in Cardiac Surgery: A Simulation Study Across 9 Italian Cardiac Centers

Application of statistical process charts has led to consistent quality production improvement in the industrial sector. Aim of this simulation study is to assess if the use of exponentially weighted moving average chart with control limits (CL) could help to identify mortality trends in a cardiac surgery scenario. Mortality rate of 9 cardiac centers has been continuously monitored by a central clinical governance unit since 2010; prospectively collected monthly mortality rate of calendar year 2013-2014 from each center was used to retrospectively build an exponentially weighted moving average chart; mortality level was set at 4% as per threshold defined by the Italian Ministry of Health recommendation; upper CLs were set as 1.5 standard deviation from the specified level; lowest mortality rate (2.6%) was observed during calendar year 2012-2013, hence that was considered the center of the chart. All centers were considered as 1 entity and consecutively plotted in the chart following a geographic distribution, from North to South. A total number of 4049 operations were performed; 108 patients died while in hospital (2.6%). Different mortality trends that consisted of minor and major out-of-control process defined as a point of the chart outside the upper CLs were demonstrated. In conclusion, mortality trends could have been potentially identified at earlier time points before reaching the 4% limits of mortality; exponentially weighted and controlled chart may facilitate clinical governance units to their monitoring role.

Big data and clinicians: a review on the state of the science

BACKGROUND

In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality.

OBJECTIVE

The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm.

METHODS

We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years.

RESULTS

This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data.

CONCLUSIONS

Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data.

Data Resource Profile: adult cardiac surgery database of the Netherlands Association for Cardio-Thoracic Surgery

In 2007 The Netherlands Association for Cardio-Thoracic Surgery (Nederlandse Vereniging voor Thoraxchirurgie, NVT) instituted the Adult Cardiac Surgery Database. The dataset comprises demographic factors, type of intervention, in-hospital mortality and 18 risk factors for mortality after cardiac surgery, according to the European System for Cardiac Operative Risk Evaluation definitions. Currently, this procedural database contains over 60 000 interventions. Completeness of data is excellent and national coverage of all 16 Dutch cardio-thoracic surgery centres has been achieved since the start. The primary goal of the database is to control and maintain the quality of care by evaluation of outcomes. This is accomplished by regular feedback and comparison of outcomes. For a subset of the database (procedures from 10 out of 16 centres) longer-term follow-up has been established by means of data linkage to two national registries. This provides information on survival status, causes of death and readmissions. The database has recently been used for research, resulting in methodological papers aimed at optimizing comparison of outcomes. In future, clinical issues will also be addressed, for example survival after coronary artery bypass grafting and valve surgery.