Is the diagnostic coding position of acute heart failure related to mortality? A report from the Euro Heart Failure Survey-1

Validity of International Classification of Diseases (ICD)-10 Diagnosis Codes for Identification of Acute Heart Failure Hospitalization and Heart Failure with Reduced Versus Preserved Ejection Fraction in a National Medicare Sample

BACKGROUND

Heart failure (HF) is a leading cause of hospitalization in older adults. Medicare data have been used to assess HF outcomes. However, the validity of ICD-10 diagnosis codes (used since 2015) to identify acute HF hospitalization or distinguish reduced (heart failure with reduced ejection fraction) versus preserved ejection fraction (HFpEF) is unknown in Medicare data.

METHODS

Using Medicare data (2015-2017), we randomly sampled 200 HF hospitalizations with ICD-10 diagnosis codes for HF in the first/second claim position in a 1:1:2 ratio for systolic HF (I50.2), diastolic HF (I50.3), and other HF (I50.X). The primary gold standards included recorded HF diagnosis by a treating physician for HF hospitalization, ejection fraction (EF)≤50 for heart failure with reduced ejection fraction, and EF>50 for HFpEF. If the quantitative EF was not present, then qualitative descriptions of EF were used for heart failure with reduced ejection fraction/HFpEF gold standards. Multiple secondary gold standards were also tested. Gold standard data were extracted from medical records using standardized forms and adjudicated by cardiology fellows/staff. We calculated positive predictive values with 95% CIs.

RESULTS

The 200-chart validation sample included 50 systolic, 50 diastolic, 47 combined dysfunction, and 53 unspecified HF patients. The positive predictive values of acute HF hospitalization was 98% [95% CI, 95-100] for first-position ICD-10 HF diagnosis and 66% [95% CI, 58-74] for first/second-position diagnosis. Quantitative EF was available for ≥80% of patients with systolic, diastolic, or combined dysfunction ICD-10 codes. The positive predictive value of systolic HF codes was 90% [95% CI, 82-98] for EFs≤50% and 72% [95% CI, 60-85] for EFs≤40%. The positive predictive value was 92% [95% CI, 85-100] for HFpEF for EFs>50%. The ICD-10 codes for combined or unspecified HF poorly predicted heart failure with reduced ejection fraction or HFpEF.

CONCLUSIONS

ICD-10 principal diagnosis identified acute HF hospitalization with a high positive predictive value. Systolic and diastolic ICD-10 diagnoses reliably identified heart failure with reduced ejection fraction and HFpEF when EF 50% was used as the cutoff.

Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure

AIMS

Risk changes with the progression of disease and the impact of treatment. We developed a dynamic risk stratification Markov chain model using artificial intelligence in patients with chronic heart failure (CHF).

METHODS AND RESULTS

We described the pattern of behaviour among 7496 consecutive patients assessed for suspected HF. The following mutually exclusive health states were defined and assessed every 4 months: death, hospitalization, outpatient visit, no event, and leaving the service altogether (defined as no event at any point following assessment). The observed figures at the first transition (4 months) weres 427 (6%), 1559 (21%), 2254 (30%), 1414 (19%), and 1842 (25%), respectively. The probabilities derived from the first two transitions (i.e. from baseline to 4 months and from 4 to 8 months) were used to construct the model. An example of the model's prediction is that at cycle 4, the cumulative probability of death was 14%; leaving the system, 37%; being hospitalized between 12 and 16 months, 10%; having an outpatient visit, 8%; and having no event, 31%. The corresponding observed figures were 14%, 41%, 10%, 15%, and 21%, respectively. The model predicted that during the first 2 years, a patient had a probability of dying of 0.19, and the observed value was 0.18.

CONCLUSIONS

A model derived from the first 8 months of follow-up is strongly predictive of future events in a population of patients with chronic heart failure. The course of CHF is more linear than is commonly supposed, and thus more predictable.

Primary vs. Secondary Heart Failure Diagnosis: Differences in Clinical Outcomes, Healthcare Resource Utilization and Cost

Background

There is scarce information on patients with secondary heart failure diagnosis (sHF). We aimed to compare the characteristics, burden, and outcomes of sHF with those with primary HF diagnosis (pHF).

Methods

Retrospective, observational study on patients ≥18 years with emergency department (ED) visits during 2018 with pHF and sHF in ED or hospital (ICD-10-CM) diagnostic codes. Baseline characteristics, 30-day and 1-year mortality, readmission and re-ED visit rates, and costs were compared between sHF and pHF.

Results

Out of the 797 patients discharged home from the ED, 45.5% had sHF, and these presented lower 1-year hospitalization, re-ED visit rates, and costs. In contrast, out of the 2,286 hospitalized patients, 55% had sHF and 45% pHF. Hospitalized sHF patients had significantly (p < 0.01) greater comorbidity, lower use of recommended HF therapies, longer length of stay (10.8 ± 10.1 vs. 9.7 ± 7.9 days), and higher in-hospital and 1-year mortality (32 vs. 25.8%) with no significant differences in readmission rates and lower 1-year re-ED visit rate. Hospitalized sHF patients had higher total costs (€12,262,422 vs. €9,144,952, p < 0.001), mean cost per patient-year (€9,755 ± 13,395 vs. €8,887 ± 12,059), and average daily cost per patient.

Conclusion

Hospitalized sHF patients have a worse initial prognosis, greater use of healthcare resources, and higher costs.

Distribution, management and outcomes of AMI according to principal diagnosis priority during inpatient admission

BACKGROUND

In recent years, there has been a growing interest in outcomes of patients with acute myocardial infarction (AMI) using large administrative datasets. The present study was designed to compare the characteristics, management strategies and acute outcomes between patients with primary and secondary AMI diagnoses in a national cohort of patients.

METHODS

All hospitalisations of adults (≥18 years) with a discharge diagnosis of AMI in the US National Inpatient Sample from January 2004 to September 2015 were included, stratified by primary or secondary AMI. The International Classification of Diseases, ninth revision and Clinical Classification Software codes were used to identify patient comorbidities, procedures and clinical outcomes.

RESULTS

A total of 10 864 598 weighted AMI hospitalisations were analysed, of which 7 186 261 (66.1%) were primary AMIs and 3 678 337 (33.9%) were secondary AMI. Patients with primary AMI diagnoses were younger (median 68 vs 74 years, P < .001) and less likely to be female (39.6% vs 48.5%, P < .001). Secondary AMI was associated with lower odds of receipt of coronary angiography (aOR 0.19; 95%CI 0.18-0.19) and percutaneous coronary intervention (0.24; 0.23-0.24). Secondary AMI was associated with increased odds of MACCE (1.73; 1.73-1.74), mortality (1.71; 1.70-1.72), major bleeding (1.64; 1.62-1.65), cardiac complications (1.69; 1.65-1.73) and stroke (1.68; 1.67-1.70) (P < .001 for all).

CONCLUSIONS

Secondary AMI diagnoses account for one-third of AMI admissions. Patients with secondary AMI are older, less likely to receive invasive care and have worse outcomes than patients with a primary diagnosis code of AMI. Future studies should consider both primary and secondary AMI diagnoses codes in order to accurately inform clinical decision-making and health planning.

Sensitivity, specificity, positive and negative predictive values of identifying atrial fibrillation using administrative data: a systematic review and meta-analysis

Introduction

Atrial fibrillation (AF) is the commonest arrhythmia and a major cause of stroke and health care utilization. Researchers and administrators use electronic health data to assess disease burden, quality and variance in care, value of interventions and prognosis. We performed a systematic review and meta-analysis to assess the validity of AF case definitions in administrative databases.

Methods

Medline was searched from 2000 to 2018. Extracted information included sensitivity, specificity, positive and negative predictive values (PPV and NPV) for various AF case definitions. Estimates were pooled using random-effects models due to significant heterogeneity between studies.

Results

We identified 24 studies, including 21 from North America or Scandinavia. Hospital, ambulatory and mixed data sources were assessed in 10, 4 and 10 studies, respectively. Nine different AF case definitions were evaluated, most based on ICD-9 or 10 codes. Twenty-two studies assessed case definitions in patients diagnosed with AF and thus could generate PPV alone. Half the studies sampled unrestricted populations including a mix of those with and without AF to assess sensitivity. Only 13 studies included ECG confirmation as a gold standard. The pooled random effects estimates were: sensitivity 80% (95% CI 72-86%); specificity 98% (96-99%); PPV 88% (82-94%); NPV 97% (94-99%). Only 3 studies reported all accuracy parameters and included rhythm monitoring in the gold standard definition.

Conclusion

Relatively few studies examined sensitivity, and fewer still included rhythm monitoring in the gold standard comparison. Administrative data may fail to identify a significant proportion of patients with AF. This, in turn, may bias estimates of quality of care and prognosis.

Mode of presentation and mortality amongst patients hospitalized with heart failure? A report from the First Euro Heart Failure Survey

BACKGROUND

Heart failure is heterogeneous in aetiology, pathophysiology, and presentation. Despite this diversity, clinical trials of patients hospitalized for HF deal with this problem as a single entity, which may be one reason for repeated failures.

METHODS

The first EuroHeart Failure Survey screened consecutive deaths and discharges of patients with suspected heart failure during 2000-2001. Patients were sorted into seven mutually exclusive hierarchical presentations: (1) with cardiac arrest/ventricular arrhythmia; (2) with acute coronary syndrome; (3) with rapid atrial fibrillation; (4) with acute breathlessness; (5) with other symptoms/signs such as peripheral oedema; (6) with stable symptoms; and (7) others in whom the contribution of HF to admission was not clear.

RESULTS

The 10,701 patients enrolled were classified into the above seven presentations as follows: 260 (2%), 560 (5%), 799 (8%), 2479 (24%), 1040 (10%), 703 (7%), and 4691 (45%) for which index-admission mortality was 26%, 20%, 10%, 8%, 6%, 6%, and 4%, respectively. Compared to those in group 7, the hazard ratios for death during the index admission were 4.9 (p ≤ 0.001), 4.0 (p < 0.001), 2.2 (p < 0.001), 2.1 (p < 0.001), 1.4 (p < 0.04) and 1.4 (p = 0.04), respectively. These differences were no longer statistically significant by 12 weeks.

CONCLUSION

There is great diversity in the presentation of heart failure that is associated with very different short-term outcomes. Only a minority of hospitalizations associated with suspected heart failure are associated with acute breathlessness. This should be taken into account in the design of future clinical trials.

Precipitating factors and 90-day outcome of acute heart failure: a report from the intercontinental GREAT registry

AIMS

Several clinical conditions may precipitate acute heart failure (AHF) and influence clinical outcome. In this study we hypothesized that precipitating factors are independently associated with 90-day risk of death in AHF.

METHODS AND RESULTS

The study population consisted of 15 828 AHF patients from Europe and Asia. The primary outcome was 90-day all-cause mortality according to identified precipitating factors of AHF [acute coronary syndrome (ACS), infection, atrial fibrillation (AF), hypertension, and non-compliance]. Mortality at 90 days was 15.8%. AHF precipitated by ACS or by infection showed increased 90-day risk of death compared with AHF without identified precipitants [hazard ratio (HR) 1.69, 95% confidence interval (CI) 1.44-1.97, P < 0.001; and HR 1.51, 95% CI 1.18-1.92, P = 0.001), while AHF precipitated by AF showed lower 90-day risk of death (HR 0.56, 95% CI 0.42-0.75, P < 0.001), after multivariable adjustment. The risk of death in AHF precipitated by ACS was the highest during the first week after admission, while in AHF precipitated by infection the risk of death had a delayed peak at week 3. In AHF precipitated by AF, a trend toward reduced risk of death during the first weeks was shown. At weeks 5-6, AHF precipitated by ACS, infection, or AF showed similar risk of death to that of AHF without identified precipitants.

CONCLUSIONS

Precipitating factors are independently associated with 90-day mortality in AHF. AHF precipitated by ACS or infection is independently associated with higher, while AHF precipitated by AF is associated with lower 90-day risk of death.

Examining Characteristics of Hospitalizations in Heart Failure Patients: Results from the 2009 All-payer Data

Heart failure (HF) is one of the most common chronic and disabling illnesses, resulting in high morbidity and mortality. Readmission rate, one key indicator of healthcare quality and healthcare utilization, is prevalent in HF patients. Inconsistent evidences exist about the impact of rural health disparities on HF patients' readmissions. The purpose of this explorative study was to examine the characteristics of hospitalized HF patients and factors related to readmissions in 2009. The results showed all-cause readmission rates were 13.6%, 23.6%, and 31.6% at 30-, 90- and 180-days respectively. The factors related readmissions included age, income, discharge/transfer status from index hospitalization, and comorbidity. Findings from this analysis suggested additional studies using multiple data sources are needed to have a comprehensive understanding of risk factors related HF patients' healthcare utilization.