{'fraction_non_alphanumeric': 0.020240963855421686, 'fraction_numerical': 0.014457831325301205, 'mean_word_length': 5.4875, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Chances and challenges of using routine data collections for renal health care research.

BACKGROUND
Collections of electronic medical records (EMRs) can provide a rich source of information for renal health care research. However, their use in statistical analyses requires many preparatory steps, including coding of freetext entries and clear definitions of time windows for harvesting prognostic factors and outcomes. We analyse a large collection of EMRs to identify prognostic factors of adequate health care in diabetic patients at risk for chronic kidney disease, and discuss benefits and risks of such re-use of routine data.


METHODS
In a representative sample of 695 068 patient records collected in 58 Austrian general practitioners' offices, we could identify 31 374 patients with diabetes mellitus. As outcomes, we investigated whether a patient received a serum creatinine measurement, and the time elapsing between two consecutive serum creatinine measurements. Prognostic factors were defined by extracting previous diagnoses, laboratory measurements, drug prescriptions and demographic characteristics from the records.


RESULTS
Serum creatinine was measured annually in 44.4% of diabetic patients with previous signs of reduced kidney function and in 20.5% of the patients without such signs. Within 1 year after the first measurement, a follow-up measurement was made in 79.4 and 68.4% of the patients, respectively. Previous diagnoses, laboratory measurements, drug prescriptions and demographic characteristics explained 41% of the observed variance of kidney function monitoring. With 24% explained variance, previous referrals to laboratories were identified as the most important prognostic factor group.


CONCLUSIONS
The analysis of large routine data collections poses various challenges, among which the need for coding free text into variables and various sources of biases are most demanding. However, routine data collections represent the daily practice of health care and offer many chances for renal health services and outcomes research.