Underestimated prevalence of heart failure in hospital inpatients: a comparison of ICD codes and discharge letter information

Abstract

Background: Heart failure is the predominant cause of hospitalization and amongst the leading causes of death in Germany. However, accurate estimates of prevalence and incidence are lacking. Reported figures originating from different information sources are compromised by factors like economic reasons or documentation quality.

Methods: We implemented a clinical data warehouse that integrates various information sources (structured parameters, plain text, data extracted by natural language processing) and enables reliable approximations to the real number of heart failure patients. Performance of ICD-based diagnosis in detecting heart failure was compared across the years 2000-2015 with (a) advanced definitions based on algorithms that integrate various sources of the hospital information system, and (b) a physician-based reference standard.

Results: Applying these methods for detecting heart failure in inpatients revealed that relying on ICD codes resulted in a marked underestimation of the true prevalence of heart failure, ranging from 44% in the validation dataset to 55% (single year) and 31% (all years) in the overall analysis. Percentages changed over the years, indicating secular changes in coding practice and efficiency. Performance was markedly improved using search and permutation algorithms from the initial expert-specified query (F1 score of 81%) to the computer-optimized query (F1 score of 86%) or, alternatively, optimizing precision or sensitivity depending on the search objective.

Conclusions: Estimating prevalence of heart failure using ICD codes as the sole data source yielded unreliable results. Diagnostic accuracy was markedly improved using dedicated search algorithms. Our approach may be transferred to other hospital information systems.

Keywords: Data warehouse; Electronic health records; Heart failure; ICD coding; Information extraction.

Fig. 1

Count of inpatients within the Department of Medicine I in the years 2000–2015. The solid line indicates all patients; each patient is counted once per year. Intermittent lines represent patients with heart failure identified using different automated heart failure detection algorithms: M_Expert originates from the variable set pre-specified by the clinical expert; A_Precision optimizes count of false positives; A_Sensitivity optimizes count of false negatives; A_F1 optimizes overall accuracy (for details refer to“Methods”)

Fig. 2

Detection of heart failure in inpatients using different approaches (percentage of all inpatients). The solid line indicates the prevalence detected when applying the automated algorithm A_F1 (for details refer to “Methods”). Intermittent lines indicate detection using ICD codes or other information tags that dominantly contributed to the detection of heart failure. Each patient entered analysis only once per year; if patients attended the hospital multiple times, the first case of each patient per year was used

Fig. 3

Detection of heart failure via related ICD codes (dark gray) and the additional detection through other search terms* (light gray), in inpatients with heart failure across the entire sampling period (years 2000–2015). The percentage of patients found via selective ICD code search increased in recent years, which might be explained by the foundation of the Comprehensive Heart Failure Center Würzburg in the year 2010, i.e., a facility devoted to the integration of research and care of patients with heart failure. *Executed via application of the automated algorithm A_F1 (for details refer to “Methods”)