Evaluating the performance of temporal pattern discovery: new application using statins and rhabdomyolysis in OMOP databases.

BACKGROUND: Temporal pattern discovery (TPD) is a method of signal detection using electronic healthcare databases, serving as an alternative to spontaneous reporting of adverse drug events. Here, we aimed to replicate and optimise a TPD approach previously used to assess temporal signals of statins with rhabdomyolysis (in The Health Improvement Network (THIN) database) by using the OHDSI tools designed for OMOP data sources. METHODS: We used data from the Truven MarketScan US Commercial Claims and the Commercial Claims and Encounters (CCAE). Using an extension of the OHDSI ICTemporalPatternDiscovery package, we ran positive and negative controls through four analytical settings and calculated sensitivity, specificity, bias and AUC to assess performance. RESULTS: Similar to previous findings, we noted an increase in the Information Component (IC) for simvastatin and rhabdomyolysis following initial exposure and throughout the surveillance window. For example, the change in IC was 0.266 for the surveillance period of 1-30 days as compared to the control period of - 180 to - 1 days. Our modification of the existing OHDSI software allowed for faster queries and more efficient generation of chronographs. CONCLUSION: Our OMOP replication matched the we can account forwe can account for of the original THIN study, only simvastatin had a signal. The TPD method is a useful signal detection tool that provides a single statistic on temporal association and a graphical depiction of the temporal pattern of the drug outcome combination. It remains unclear if the method works well for rare adverse events, but it has been shown to be a useful risk identification tool for longitudinal observational databases. Future work should compare the performance of TPD with other pharmacoepidemiology methods and mining techniques of signal detection. In addition, it would be worth investigating the relative TPD performance characteristics using a variety of observational data sources.

Clinical assessment to determine the risk of bowel cancer using Symptoms, Age, Mass and Iron deficiency anaemia (SAMI).

BACKGROUND: The aim of this study was to identify characteristics with independent predictive value for bowel cancer for use in the clinical assessment of patients attending colorectal outpatient clinics. METHODS: This was a 22-year (1986-2007) retrospective cohort analysis of data collected prospectively from patients who attended colorectal surgical outpatient clinics in Portsmouth. The data set was split randomly into two groups of patients to generate and validate a predictive model. Multivariable logistic regression was used to create and validate a system to predict outcome. Receiver operating characteristic (ROC) curves and Hosmer-Lemeshow test were used to evaluate the model's predictive capability. The likelihood of bowel cancer was expressed as the odds ratio (OR). RESULTS: Data from 29 005 patients were analysed. Discrimination of the model for bowel cancer was high in the development (C-statistic 0·87, 95 per cent c.i. 0·85 to 0·88) and validation (C-statistic 0·86, 0·84 to 0·87) groups. The most important co-variables in the final model were: age (OR 3·17-27·10), rectal (OR 31·48) or abdominal (OR 1·83-8·45) mass, iron deficiency anaemia (IDA) (OR 4·42-8·38), rectal bleeding and change in bowel habit in combination (OR 5·37), change in bowel habit without rectal bleeding, with or without abdominal pain (OR 2·12-2·52), and rectal bleeding with no perianal symptoms and without change in bowel habit (OR 2·91). Some 91·5 per cent of bowel cancers presented with these characteristics, 40·4 per cent with a mass and/or IDA. In patients with at least one of these characteristics the overall risk of having cancer was 10·0 (range 6·5-50·4) per cent, compared with 1·1 (0·3-2·3) per cent in patients without them. CONCLUSION: A clinical assessment that systematically identifies or excludes four symptom-age combinations, a mass and IDA (SAMI) stratifies patients as having a low and higher risk of having bowel cancer. This could improve patient selection for referral and investigation.

Is earlier referral and investigation of bowel cancer patients presenting with rectal bleeding associated with better survival?

AIM: This study was carried out to determine whether rectal bleeding is related to stage of bowel cancer and whether earlier diagnosis and treatment are associated with improved survival. METHOD: Eight hundred and forty-five patients were identified in the Wessex Bowel Cancer Audit (1991-1994). Presenting symptoms were identified from case notes. Outcome measures included 5-year survival, Dukes' stage, metastatic disease at surgery and time from onset of symptoms to treatment, in patients presenting with rectal bleeding or other symptoms and signs. RESULTS: Six hundred and seventy-six (80%) of 845 patient case notes were reviewed. Of these, 408 (60.4%) patients had rectal or sigmoid cancer, and 255 (62.5%) of these 408 patients, who presented with rectal bleeding, had significantly earlier stage disease than those with a change in bowel habit and/or abdominal pain (Dukes' stage A: 23.1%vs 3.6%; Dukes' stage D: 14.5%vs 23.4%; P < 0.001), fewer metastases visible at surgery (14.9%vs 22.6%; P < 0.001) and significantly better 5-year survival (54.8%vs 40.9%; P < 0.001). There was no further significant improvement in 5-year survival in patients treated within 6 months of the onset of symptoms (55.1%vs 53.5%). Hazard ratios showed that 5-year survival was independently associated with age, Dukes' stage and emergency treatment, but not with rectal bleeding, change in bowel habit, abdominal pain or delay in treatment. CONCLUSION: Bowel cancer patients presenting with rectal bleeding had earlier stage disease and significantly better survival than patients presenting with a change in bowel habit or abdominal pain. There was no reduction in 5-year survival in those patients who had a delay in treatment for > 6 months from the onset of symptoms.

Weekend admission and mortality from acute exacerbations of chronic obstructive pulmonary disease in winter.

Historically, acute medical staffing numbers have been lower on weekends and in winter numbers of medical admissions rise. An analysis of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) admissions to Portsmouth Hospitals over a seven-year period was undertaken to examine the effects of admission on a weekend, of winter, and with the opening of a medical admissions unit (MAU). In total, 9,915 admissions with AECOPD were identified. Weekend admissions accounted for 2,071 (20.9%) of cases, winter accounted for 3,026 (30.5%) admissions, and 522 (34.4%) deaths. Adjusted odds ratio (OR) for death on day 1 after winter weekend admission was 2.89 (95% confidence interval (CI) 1.035 to 8.076). After opening the MAU, the OR for death day 1 after weekend winter admission fell from 3.63 (95% CI 1.15 to 11.5) to 1.65 (95% CI 0.14 to 19.01). AECOPD patients have an increased risk of death after admission over a weekend in winter and this effect was reduced by opening a MAU. These findings have implications for the planning of acute care provision in different seasons.

Structured assessment for prospective identification of safety signals in electronic medical records: evaluation in the health improvement network.

BACKGROUND: Pharmacovigilance signal detection largely relies on individual case reports, but longitudinal health data are being explored as complementary information sources. Research to date has focused on the ability of epidemiological methods to distinguish established adverse drug reactions (ADRs) from unrelated adverse events. OBJECTIVE: The aim of this study was to evaluate a process for structured clinical and epidemiological assessment of temporally associated drugs and medical events in electronic medical records. METHODS: Pairs of drugs and medical events were selected for review on the basis of their temporal association according to a calibrated self-controlled cohort analysis in The Health Improvement Network. Six assessors trained in pharmacovigilance and/or epidemiology evaluated seven drugs each, with up to 20 medical events per drug. A pre-specified questionnaire considered aspects related to the nature of the temporal pattern, demographic features of the cohort, concomitant medicines, earlier signs and symptoms, and possible confounding by underlying disease. This informed a classification of drug-event pairs as known ADRs, meriting further evaluation, or dismissed. RESULTS: The number of temporally associated medical events per drug ranged from 11 to 307 (median 50) for the 42 selected drugs. Out of the 509 relevant drug-event combinations subjected to the assessment, 127 (25 %) were classified as known ADRs. Ninety-one (24 %) of the remaining pairs were classified as potential signals meriting further evaluation and 291 (76 %) were dismissed. Suggestive temporal patterns and lack of clear alternative explanations were the most common reasons that drug-event pairs were classified as meriting further evaluation. Earlier signs and symptoms and confounding by the underlying disease were the most common reasons that drug-event pairs were dismissed. CONCLUSIONS: Exploratory analysis of electronic medical records can detect important potential safety signals. However, effective signal detection requires that statistical signal detection be combined with clinical and epidemiological review to achieve an acceptable false positive rate.