Predictability of Inpatient Mortality of Different Comorbidities in Both Types of Acute Decompensated Heart Failure
Methods
We conducted a retrospective cohort study utilizing the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS) 2016 database.
Results
There were totally 116,189 admissions for acute decompensated heart failure (ADHF). Of these, 50.9% were for heart failure with reduced ejection fraction (HFrEF) group (n = 59,195), and 49.1% were for heart failure with preserved ejection faction (HFpEF) group (n = 56,994). Overall, in-hospital mortality was 2.5% of admissions for ADHF (n = 2,869). When stratified by HF types, admissions for HFrEF had higher mortality rate (2.7%, n = 1,594) in comparison to admissions for HFpEF (2.2%, n = 1,275) (P < 0.001). Significantly associated variables in univariate analyses were age, race, hypertension, diabetes mellitus, chronic kidney disease (CKD), atrial fibrillation/flutter, obesity, and chronic ischemic heart disease (IHD), while gender and chronic obstructive pulmonary disease (COPD) did not achieve statistical significance (P > 0.1).
Conclusions
To our knowledge, this is the first study to stratify HF patients based on ejection fraction and utilizing different predictors and in-hospital mortality. These and other data support the need for future research to utilize these predictors to create more accurate models in the future.Keywords: Heart failure, In-hospital mortality, Ejection fraction, HFrEF, HFpEF
Introduction
Heart failure (HF) is a complex clinical syndrome that typically presents with either fluid overload or exercise intolerance. It can be caused by impaired left ventricle (LV) filling, impaired ejection of blood, or coexistence of both mechanisms. Ejection fraction (EF) has become the main determinant to differentiate between two major types of HF, heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection faction (HFpEF). It was estimated that 5.7 million Americans suffer from HF with projected increase in prevalence to exceed 8 million patients by 2030. Therefore, it remains a major direct and indirect cause of morbidity, mortality, and medical costs.
Several prediction models were developed to predict the short-term outcomes of HF. In addition to providing prognostic information, such models can play a crucial role in the management of certain patients, especially when outcomes are predicted to be poor and early palliative consult and/or hospice referral become more reasonable. Most of those prediction models shared similar variables, renal function, age, and blood pressure being the most studied variables. However, these modules have been underutilized in the daily clinical practice due to their limited accuracy in predicting serious events. In addition to their limited ability to include all potential comorbidities, these models did not take into consideration the different types of HF. We sought to investigate the predictability of commonly associated comorbidities with in-hospital mortality among HF cohort. In addition to investigate whether the predictability, if any exist, is different between the two major types of HF, to be one step closer towards producing a model with better predictability.
Materials and Methods
Strobe guidelines were sought for reporting this manuscript.
Study design/settings
We conducted a retrospective cohort study utilizing the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS) 2016 database. The database included a stratified sample of 20% of all-payer inpatient encounters in the USA. The encounters included in this database were systematically selected by the Agency for Healthcare Resources and Quality (AHRQ) to be representative of all the hospitalizations on the national level. The reported variables in this database include demographic variables, primary and secondary admission diagnoses, procedures, disposition, length of stay, and inpatient mortality, among others.
Participants
Encounters included in this study were hospitalizations for patients who were admitted primarily for acute HF, both systolic and diastolic. Patients with age < 18 year were excluded from the study. In addition, patient who have combined systolic and diastolic dysfunction were excluded to facilitate a direct comparison.
Variables
We sought to investigate demographic variables (age, sex, and race), and associated comorbidities/conditions (hypertension, chronic ischemic heart disease (IHD), diabetes mellitus, atrial fibrillation/flutter, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), obesity, hypertensive crisis, cardiogenic shock, acute coronary syndrome, pneumonias excluding influenza, and influenza). Primary outcome sought was inpatient mortality.
Data measurement
Clinical conditions, including acute decompensated heart failure (ADHF) (and its types) and concurrent comorbidities/conditions, were identified through their international classification of diseases, 10th revision (ICD-10 codes) that were recorded in the discharge record for each hospitalization.
Ethical considerations
No institutional review board (IRB) approval was obtained as the data, on the national level, are completely de-identified. The study was conducted in compliance with the ethical standards of the responsible institution on human subjects as well as with the Helsinki Declaration.
Statistical methods
Kolmogorov-Smirnov test was used to examine normality of continuous variables. Non-parametric continuous and categorical variables were described as median with interquartile range and frequencies, as appropriate. Chi-square test was used to compare categorical variables, and Mann-Whitney U test was used to compare non-parametric continuous variables. Variables associated with outcome in univariate analysis (P value < 0.10) were included in a logistic regression model (enter) to determine predictors of mortality. All analyses were done using IBM SPSS StatisticsTM version 26.0 (IBM Corporation, Artmonk, NY). An alpha value (P) of 0.01 was used to ascertain statistical significance given the large sample size.
Results
There were a total of 116,189 admissions for ADHF. Of these, 50.9% were admissions for HFrEF group (n = 59,195) and 49.1% were for HFpEF group (n = 56,994).
Missing data
Age, death during hospitalization, and gender had negligible missing values (0.0% (n = 6), 0.0% (n = 88), 0.0% (n = 39), respectively), while race was missing 3.35% (n = 3,768) of the data. Missingness of race data statistically correlated to age, discharge quarter, gender, and hospital division as well as other auxiliary variables (median household income for patient’s zone improvement plan (ZIP) code and expected primary insurance) likely indicating data were missing not at random (MAR); however, imputation of missing data was not conducted as missing less than 5% of data are unlikely to introduce bias.
Baseline characteristics
Baseline characteristics are summarized in Table 1. Age distribution did not follow normal distribution. Median age was 74 years with a 25th – 75th interquartile range of 62 – 84 years. Female to male ratio was 1:1. White was the most prevalent race. Among the comorbidities explored in the study, hypertension was the most common.
Table 1
Baseline Characteristics of Admitted Patients
| Median (interquartile range) or number (%) | |
|---|---|
| Age (years) | 74 (62 – 84) |
| Sex | |
| Male | 58,525 (50.4) |
| Female | 57,625 (49.6) |
| Race | |
| White | 77,318 (66.5) |
| Black | 21,313 (18.3) |
| Hispanic | 8,521 (7.3) |
| Asian or Pacific Islander | 2,233 (1.9) |
| Native American | 498 (0.4) |
| Other | 2,538 (2.2) |
| Comorbidities | |
| Hypertension | 96,133 (82.7) |
| Chronic IHD | 59,486 (51.2) |
| Diabetes mellitus | 54,076 (46.5) |
| Atrial fibrillation/flutter | 54,170 (46) |
| CKD | 52,314 (45) |
| COPD | 42,221 (36) |
| Obesity | 27,359 (23.5) |
IHD: ischemic heart diseases; CKD: chronic kidney disease; COPD: chronic obstructive pulmonary disease.
Inpatient mortality
Overall, 2.5% of admissions for ADHF had death as outcome (n = 2,869). When stratified by HF types, admissions for HFrEF had higher mortality rate (2.7%, n = 1,594) than admissions for HFpEF (2.2%, n = 1,275) (P < 0.001).
Predictors of inpatient mortality
Inpatient mortality differed across different clinical characteristics (Table 2). Significantly associated variables in univariate analyses were age, race, hypertension, diabetes mellitus, CKD, atrial fibrillation/flutter, obesity, and chronic IHD; while gender and COPD did not achieve statistical significance (P > 0.1). Therefore, these factors were included in a multivariate logistic regression model, which showed that an increase of 10 years in age would increase the odds of inpatient mortality by 20%, and the presence of atrial fibrillation/flutter and CKD would increase the odds of inpatient mortality by 27% and 66%, respectively. While black race, hypertension, diabetes mellitus, and obesity would decrease the odds of inpatient mortality by 30%, 47%, 12%, 27%, respectively (Table 3).
Table 2
Correlations Between Inpatient Mortality and Different Clinical Characteristics
| Outcomes, frequency (%) | P value | ||
|---|---|---|---|
| Died | Discharged alive | ||
| Age (mean rank) | 70,558.94 | 57,730.99 | < 0.001 |
| Gender | |||
| Male | 1,462 (2.5) | 57,022 (97.5) | 0.52 |
| Female | 1,406 (2.4) | 56,172 (97.6) | |
| Race | |||
| White | 2,160 (2.8) | 75,089 (97.2) | < 0.001 |
| Black | 300 (1.4) | 20,997 (98.6) | |
| Hispanic | 163 (1.9) | 8,356 (98.1) | |
| Asian or Pacific Islander | 67 (3) | 2,166 (97) | |
| Native American | 13 (2.6) | 485 (97.4) | |
| Other | 49 (1.9) | 2,489 (98.1) | |
| Hypertension | |||
| Yes | 2,131 (2.2) | 93,937 (97.8) | < 0.001 |
| No | 738 (3.7) | 19,295 (96.3) | |
| Chronic IHD | |||
| Yes | 1,514 (2.5) | 57,934 (97.5) | 0.08 |
| No | 1,355 (2.4) | 55,298 (97.6) | |
| Diabetes mellitus | |||
| Yes | 1,143 (2.1) | 52,893 (97.9) | < 0.001 |
| No | 1,726 (2.8) | 60,339 (97.2) | |
| Atrial fibrillation/flutter | |||
| Yes | 1,672 (3.1) | 52,450 (96.9) | < 0.001 |
| No | 1,197 (1.9) | 60,782 (98.1) | |
| CKD | |||
| Yes | 12,819 (5) | 244,420 (95) | < 0.001 |
| No | 13,008 (4.1) | 303,566(95.9) | |
| COPD | |||
| Yes | 1,068 (2.5) | 41,123 (97.5) | < 0.32 |
| No | 1,801 (2.4) | 72,109 (97.6) | |
| Obesity | |||
| Yes | 417 (1.5) | 26,926 (98.5) | < 0.001 |
| No | 2,452 (2.8) | 86,306 (97.2) |
IHD: ischemic heart diseases; CKD: chronic kidney disease; COPD: chronic obstructive pulmonary disease.
Table 3
Multivariate Logistic Regression Model to Predict Inpatient Mortality of Acute Decompensated Heart Failure
| N (admissions) | OR | 99% CI for OR | P value | |
|---|---|---|---|---|
| Age | 116,183 | 1.022 | 1.017 – 1.027 | < 0.001 |
| Race | ||||
| White | 77,318 | 1 | Ref | |
| Black | 21,313 | 0.70 | 0.59 – 0.83 | < 0.001 |
| Hispanic | 8,521 | 0.84 | 0.67 – 1.04 | 0.03 |
| Asian or Pacific Islander | 2,233 | 1.13 | 0.81 – 1.56 | 0.33 |
| Native American | 498 | 1.17 | 0.56 – 2.44 | 0.56 |
| Other | 2,538 | 0.79 | 0.54 – 1.16 | 0.12 |
| Hypertension | 96,133 | 0.53 | 0.47 – 0.60 | < 0.001 |
| Chronic IHD | 59,486 | 0.99 | 0.90 – 1.10 | 0.97 |
| Diabetes mellitus | 54,076 | 0.88 | 0.79 – 0.98 | 0.003 |
| Atrial fibrillation/flutter | 54,170 | 1.27 | 1.14 – 1.41 | < 0.001 |
| CKD | 52,314 | 1.66 | 1.50 – 1.85 | < 0.001 |
| Obesity | 27,359 | 0.73 | 0.63 – 0.85 | < 0.001 |
IHD: ischemic heart diseases; CKD: chronic kidney disease; COPD: chronic obstructive pulmonary disease; OR: odds ratio; CI: confidence interval.
Then, we stratified the data by EF. Variables statistically related in univariate analyses (P < 0.1) to inpatient mortality due to admissions for HFrEF were age, race, hypertension, diabetes mellitus, CKD, obesity, atrial fibrillation/flutter, and chronic IHD, but not gender or COPD. While variables associated with inpatient mortality of HFpEF, in univariate analyses, were age, race, hypertension, diabetes mellitus, atrial fibrillation/flutter, CKD, obesity, and COPD. Results of multivariable logistic regression models for each type are shown in Table 4. Notably, Black race specifically decreased odds of inpatient mortality of patients with HFrEF, but not with HFpEF, while diabetes mellitus specifically decreased odds of patients with HFpEF, not with HFrEF. Similarly, obesity and COPD independently predicted lower and higher inpatient morality for patients with HFpEF, but not with HFrEF; while age, hypertension, atrial fibrillation/flutter, and CKD independently predicted inpatient mortality in both HFrEF and HFpEF.
Table 4
Multivariate Logistic Regression Model to Predict Inpatient Mortality of Acute Decompensated Heart Failure Types
| HFrEF | HFpEF | |||||
|---|---|---|---|---|---|---|
| OR | 99% CI for OR | P value | OR | 99% CI for OR | P value | |
| Age | 1.02 | 1.01 – 1.02 | < 0.001 | 1.03 | 1.02 – 1.03 | < 0.001 |
| Race | ||||||
| White | 1 | Ref | Ref | |||
| Black | 0.63 | 0.51 – 0.78 | < 0.001 | 0.78 | 0.56 – 1.03 | 0.02 |
| Hispanic | 0.80 | 0.60 – 1.06 | 0.04 | 0.89 | 0.64 – 1.25 | 0.38 |
| Asian or Pacific Islander | 1.02 | 0.65 – 1.60 | 0.92 | 1.30 | 0.81 – 2.10 | 0.14 |
| Native American | 1.22 | 0.50 – 2.95 | 0.55 | 1.06 | 0.29 – 3.95 | 0.89 |
| Other | 0.71 | 0.43 – 1.17 | 0.08 | 0.91 | 0.51 – 1.63 | 0.70 |
| Hypertension | 0.56 | 0.48 – 0.66 | < 0.001 | 0.51 | 0.42 – 0.61 | < 0.001 |
| Chronic IHD | 0.98 | 0.85 – 1.13 | 0.69 | Not included in the model | ||
| Diabetes mellitus | 0.93 | 0.81 – 1.08 | 0.22 | 0.84 | 0.71 – 0.99 | 0.006 |
| Atrial fibrillation/flutter | 1.25 | 1.09 – 1.44 | < 0.001 | 1.29 | 1.10 – 1.51 | < 0.001 |
| CKD | 1.75 | 1.51 – 2.01 | < 0.001 | 1.57 | 1.35 – 1.83 | < 0.001 |
| Obesity | 0.87 | 0.70 – 1.07 | 0.93 | 0.70 | 0.57 – 0.87 | < 0.001 |
| COPD | Not included in the model | 1.241 | 1.06 – 1.44 | < 0.001 |
HFrEF: heart failure with reduced ejection fraction; HFpEF: heart failure with preserved ejection faction; IHD: ischemic heart diseases; CKD: chronic kidney disease; COPD: chronic obstructive pulmonary disease; OR: odds ratio; CI: confidence interval.
Author information
aDepartment of Medicine, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
bDepartment of Medicine, Jersey Shore University Medical Center, Neptune, NJ, USA
cDepartment of Cardiology, Jersey Shore University Medical Center, Neptune, NJ, USA
dDepartment of Acute and Continuing Care, The University of Texas Health Science Center at Houston, Houston, TX, USAeDepartment of Medicine, The University of Texas Medical Branch at Galveston, Galveston, TX, USAfCritical Care Services, United Memorial Medical Center/United General Hospital, Houston, TX, USA
gCorresponding Author: Steven Douedi, Department of Medicine, Jersey Shore University Medical Center, Neptune, NJ, USA.