It is common clinical practice for physicians to refer to specific diagnostic criteria for day-to-day decision-making. In particular, whether or not to provide a particular treatment is often determined by the cutoff value of a relevant diagnostic marker. Regression discontinuity design (RDD) is a method for evaluating scenarios where intervention is determined by the certain cutoff value (e.g., threshold) of a continuous variable. RDD represents a powerful method for assessing intervention effects and outcomes. RDD is underutilized in clinical research and there are many opportunities to apply RDD in this setting. This article introduces the principles of RDD and provides examples of clinical studies that have used this design.

BACKGROUND

Although outpatient cardiac rehabilitation has been shown to be effective, the participation status of older cardiac patients is unclear in real-world settings. We investigated the proportion and associated factors of outpatient cardiac rehabilitation participation among older patients with heart diseases after cardiac intervention.

METHODS

We analyzed data from medical and long-term care insurance claims data from two municipalities in Japan. The data coverage period was between April 2014 and March 2019 in City A and between April 2012 and November 2016 in City B. We identified patients aged ≥65 years with post-operative acute myocardial infarction, angina pectoris, or heart valve disease. We estimated the proportion of cardiac rehabilitation participation and conducted logistic regression to identify factors (age, sex, type of cardiac disease, open-heart surgery, Charlson comorbidity index, long-term care need level, catecholamine use, inpatient cardiac rehabilitation, and hospital volume for cardiac rehabilitation) associated with outpatient cardiac rehabilitation participation.

RESULTS

A total of 690 patients were included in this study. The proportion of patients receiving outpatient cardiac rehabilitation was 9.0% overall. Multivariable logistic regression analysis suggested that men (adjusted OR 3.98; 95% CI 1.69–9.37), acute myocardial infarction (adjusted OR 2.76; 95% CI 1.20–6.36; reference angina pectoris), inpatient cardiac rehabilitation (adjusted OR 17.01; 95% CI 5.33–54.24), and “hospital volume” for cardiac rehabilitation (adjusted OR 4.35; 95% CI 1.14–16.57 for high-volume hospitals; reference low-volume hospital) were independently associated with outpatient cardiac rehabilitation.

CONCLUSIONS

The participation rate of outpatient cardiac rehabilitation among older post-operative cardiac patients was suboptimal. Further studies are warranted to examine its generalizability and whether a targeted approach to a group of patients who are less likely to receive outpatient cardiac rehabilitation could improve the participation rate.

BACKGROUND

This retrospective observational study validated case-finding algorithms for malignant tumors and serious infections in a Japanese administrative healthcare database.

METHODS

Random samples of possible cases of each disease (January 2015–January 2018) from two hospitals participating in the Medical Data Vision Co., Ltd. (MDV) database were identified using combinations of ICD-10 diagnostic codes and other procedural/billing codes. For each disease, two physicians identified true cases among the random samples of possible cases by medical record review; a third physician made the final decision in cases where the two physicians disagreed. The accuracy of case-finding algorithms was assessed using positive predictive value (PPV) and sensitivity.

RESULTS

There were 2,940 possible cases of malignant tumor; 180 were randomly selected and 108 were identified as true cases after medical record review. One case-finding algorithm gave a high PPV (64.1%) without substantial loss in sensitivity (90.7%) and included ICD-10 codes for malignancy and photographing/imaging. There were 3,559 possible cases of serious infection; 200 were randomly selected and 167 were identified as true cases after medical record review. Two case-finding algorithms gave a high PPV (85.6%) with no loss in sensitivity (100%). Both case-finding algorithms included the relevant diagnostic code and immunological infection test/other related test and, of these, one also included pathological diagnosis within 1 month of hospitalization.

CONCLUSIONS

The case-finding algorithms in this study showed good PPV and sensitivity for identification of cases of malignant tumors and serious infections from an administrative healthcare database in Japan.