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Clinical & Experimental Cardiology

Clinical & Experimental Cardiology
Open Access

ISSN: 2155-9880

Research Article - (2023)Volume 14, Issue 7

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Trends, In-Hospital Outcomes, and Independent Predictors of Acute Kidney Injury in Patients Admitted for the Management of Myocardial Infarction with Percutaneous Coronary Intervention: An Insight from the National Inpatient Sample Database

Akanimo Antia1*, Daniel Ubokudom2, Olanrewaju Adabale3, Ovie Okorare4, Emmanuel Daniel5, Endurance Evbayekha6, Chinwendu Angel Onuegbu7 and Kenneth Ong8
 
*Correspondence: Akanimo Antia, Department of Medicine, Lincoln Medical Center, New York, USA, Email:

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Abstract

Background: Acute Kidney Injury (AKI) is an important risk factor associated with adverse outcomes in cardiovascular illnesses, more importantly, Myocardial Infarction (MI). This study describes the Trends, in-hospital outcomes, and independent predictors of Acute Kidney Injury (AKI) in patients admitted for Myocardial infarction with Percutaneous Coronary Intervention (PCI).

Methods: This retrospective study used patient records from the 2016-2020 National In-patient Database (NIS). We identified patients who were admitted for the management of an MI who had a PCI procedure and an AKI and evaluated their associated socio-demographic and comorbid factors using International Classification of Diseases-10 (ICD-10) codes. The chi-square test was used to compare baseline characteristics between our populations with and without AKI and outcomes and multivariate logistic regression to identify independent predictors of AKI.

Results: There were 1,551,630 patients admitted for an MI and PCI, with 15% having an AKI. We observed that our population with AKI were older on admission and were more likely to be whites than blacks. A higher percentage were males. Our subpopulation was likely to have heart failure, atrial fibrillation, coronary artery disease, obesity, CKD and Charlson comorbidity index ≥ 3. A diagnosis of AKI was associated with higher in-hospital mortality rates (adjusted Odds Ratio (aOR): 2.84, CI: 2.7-3.02, p<0.001), longer mean Length Of Stay (LOS) and higher hospital costs. We noted an increasing trend in the percentage of patients who had an AKI, from about 13.5% in 2016 to 16.5% in 2020.

Conclusions: Acute Kidney Injury is strongly associated with worse hospital outcomes in patients admitted for MI and PCI, with higher mortality rates, a longer mean length of stay, and a higher hospitalization cost. A more concise look at preventive measures is recommended to minimize these outcomes.

Keywords

Acute kidney injury; Percutaneous coronary intervention; Myocardial infarction; Temporal trends; Mortality

Abbreviations

ACEI: Angiotensin Converting Enzyme Inhibitor; ACS: Acute Coronary Syndrome; AKI: Acute Kidney Injury; AMI: Acute Myocardial Injury; aOR: adjusted Odds Ratio; ARB: Angiotensin Receptor Blockers; CABG: Coronary Artery Bypass Graft; CCI: Charlson Comorbidity Index; CHF: Congestive Heart Failure; CI: Confidence Interval; CKD: Chronic Kidney Disease; COPD: Chronic Obstructive Pulmonary Disease; HCUP: Healthcare Cost and Utilization Project; KIDGO: Kidney Disease Improving Global Outcomes; PCI: Percutaneous Coronary Intervention; STEMI: ST-Elevation Myocardial Infarction

Introduction

Acute Kidney Injury (AKI) after Percutaneous Coronary Intervention (PCI) is a common complication occurring in a large percentage of STEMI patients who undergo PCI [1,2]. The use of more contrast agents during the procedure has been linked to a higher incidence of AKI after primary PCI versus elective PCI [3,4]. Emergent primary Percutaneous Coronary Intervention (primary PCI) remains the gold-standard ST-segment Elevation Myocardial Infarction (STEMI) treatment. Recent studies have shown improved outcomes and prognosis for patients with STEMI, with one-year mortality at 11% [5].

AKI can occur in patients with both pre-existing impaired renal function and in patients with normal baseline renal function [6]. In general, Acute Myocardial Infarction (AMI) is one of the critical conditions that can trigger AKI. The most common etiologies stem from hemodynamic instability causing renal hypo-perfusion, radiocontrast toxicity, and atheroembolism. About 5% of patients undergoing PCI experience a transient increase in the plasma creatinine value of more than 1.0 mg/dl because of contrast use. The risk is greatest in patients with a history of diabetes mellitus and moderate to severe renal dysfunction.

In the past decades, the morbidity of AKI has increased from about 0.3-0.5% in the United States [7]. More so, Acute Myocardial Infarction (AMI) complicated by cardiogenic shock may increase the incidence of AKI by more than 50% [8]. Among patients with AMI, those with AKI had a 20 to 40-fold higher mortality rate in comparison to those without AKI. Patients with AKI also have prolonged hospital stays, increased hospital charges, and more long-term complications, including recurrent AMI, heart failure, chronic kidney disease progression, and long-term mortality [8,9]. The aim of our retrospective study is to describe the trends, in- hospital outcomes, and independent predictors of acute kidney injury in patients admitted for myocardial infarction with percutaneous coronary intervention.

Methodolgy

This study is reported following the Strengthening of the Reporting of Observational studies in Epidemiology (STROBE) reporting guidelines.

Study design

Data source: In this retrospective study, we analyzed hospitalizations between January 1st, 2016, and December 31st, 2020, from the National Inpatient Sample (NIS) in the United States (US). The NIS was created and is maintained by the Agency for Healthcare Research and Quality and is the largest publicly available all-payer in-patient database in the United States of America (USA). It was designed as a stratified probability sample representing all non-federal acute care hospitals nationwide. Hospitals are stratified according to ownership/ control, bed size, teaching status, urban/rural location, and geographic region.

A multistage 20% probability sample of all hospitals within each stratum is then collected. All discharges from these hospitals are recorded and then weighted to ensure they are nationally representative. Data from 47 state-wide data organizations (46 States plus the District of Columbia) encompassing more than 97% of the US population is included in the NIS 2016-2020 sampling frame. As many as 30 discharge diagnoses for each hospitalization were recorded using the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD10-CM) in NIS 2016, and 40 discharge diagnoses and 25 procedures were coded in the NIS 2020 database. In the NIS, diagnoses are divided into principal and secondary Supplementary Table 1. A principal diagnosis was the main ICD-10 code for hospitalization. Secondary diagnoses were any ICD-10 code other than the principal diagnosis. Since all patient data in NIS are de-identified and publicly available, we waived the institutional review board approval.

Inclusion criteria and study variables

The study population consisted of all in-patient hospitalizations with a primary diagnosis or secondary diagnosis of Acute Kidney Injury (AKI) during admission for myocardial infarction and a coronary percutaneous intervention recorded between 2016 and 2020 following the exclusion of patients below 18 years. The diagnosis was identified using ICD-10 codes recommended by the American college of and the American Association of Cardiology respectively.

Demographic Study variables, including Age, race, Median household income, Primary insurance, and co-morbidities (computed from the Charlson comorbidities index), were identified as variables already present in the data set. Other variables were identified using The American ICD10-CM (diagnosis) medical billing codes identified from the review of other nationwide studies on cardiovascular diseases (Table 1).

Outcomes

We compared the sociodemographic differences in the population with and without AKI as highlighted in Table 1. We further analyzed the trend in the incidence of AKI and outcomes inclusive of (cardiogenic shock, cardiac arrest, and in-hospital mortality) across the years, from January 1st, 2016, to December 31st, 2020.

Variables Myocardial infarction and percutaneous intervention N=1,551,630 (%) P-values
AKI N=232,180 (15%) No AKI N=1,319,450 (85%)  
Patient characteristics - - -
Age, years, mean ± SE 68.9 ± 0.1 63.5 ± 0.03 <0.0001
Sex - - <0.001
Male 67.5 68.3 -
Female 32.5 31.7 -
Race - - <0.0001
White 71.3 76.54 -
Black 12.21 8.97 -
Hispanic 9.4 8.1 -
Asian 3.1 2.6 -
Native American 0.6 0.7 -
Other 0.3 0.3 -
Charlson comorbidity index score - - <0.0001
1 9.6 34.2 -
2 16.1 30.2 -
≥ 3 74.3 35.6 -
Comorbidities - - -
Congestive heart failure 53.8 21.1 <0.0001
Atrial fibrillation 24.61 12.04 <0.0001
CKD 49.2 11.7 <0.0001
Anemia 34.4 10.8 <0.0001
COPD 18.5 12.9 <0.0001
Obesity 21.7 19.9 <0.0001
History of CABG 9 7.14 <0.0001
Carotid artery disease 2.5 1.4 <0.0001
Dyslipidemia 67.3 71.6 <0.0001
Smoking 26.8 27.6 0.0018
Dialysis dependent 1.3 2.5 <0.0001
Hypertension 23.4 53.6 <0.0001
Diabetes 12 17.9 <0.0001

Table 1: Socio-demographic and Co-morbid differences in baseline characteristics of patients admitted for MI and PCI with and without AKI.

Variables Independent predictors of AKI
(aOR; CI) p-values
CKD 5.296; 5.1-5.49 <0.001
Cardiogenic shock 4.85; 4.68-5.0 <0.001
Cardiac arrest 3.09; 2.93-3.27 <0.001
CCI </=3 2.25; 2.15-2.35 <0.001
CHF 2.06; 2.0-2.1 <0.001

Table 2: Independent predictors of AKI in patients admitted for MI and a PCI (multivariate analysis).

Data analysis

Our data was analyzed using STATA, version 17 (Stata Corp, Texas, USA). We performed all analyses using weighted samples for national estimates following the Healthcare Cost and Utilization Project (HCUP) regulations for using NIS databases. Based on descriptive analysis of our data, continuous variables were presented with mean, and standard deviation and the differences were tested using a T-test. Categorical variables were presented with numbers (percentages) and compared with the chi-square test. Trends in incidence, mortality, and complications of AKI in our population were accounted for using conditional marginal effects of the variable "YEARS." Outcomes were analyzed to obtain adjusted Odds Ratios (aOR) using multiple logistic regression and linear regression models to account for potential confounders. The P-values considered significant in the multivariate analysis were two-sided, with<0.05 as the threshold for statistical significance.

Results

There were 1,551,630 patients admitted for an MI and PCI, with about 232,180 (15%) having an AKI compared to 1,319,450 (85%) without AKI. Our patients with AKI were notedly older with a mean age of 68.9 years as compared to those without AKI with a male predominance in both groups. The white race (75.8%) was more represented than blacks (9.5%) but when isolating each race, AKI was more common in the blacks (12.2% vs. 8.97%) than in whites (71.3% vs. 76.5%) (p<0.0001).

Our subpopulation was likely to have heart failure (53.8% vs. 21%, p<0.0001), atrial fibrillation (24.6% vs. 12%, p>0.0001), coronary artery disease (2.5% vs. 1.44%, p<0.0001), COPD (18% vs. 12.5%, p>0.0001), old-CABG (9.6% vs. 7.14%, p<0.0001), obesity (21.7% vs. 19.9%, p<0.0001), CKD (49.2% vs. 11.73%, p<0.0001), Charlson comorbidity index ≥ 3 (74.3% vs. 35.6%, p<0.0001) and anaemia (34.4% vs. 10.9%, p<0.0001). They were also more likely to be obese. Our population without a history of AKI had higher prevalence of Dyslipidaemia (71.6% vs. 67.3%), Nicotine use (27.6% vs. 26.8%), Haemodialysis dependence (2.5% vs. 1.3%) and Diabetes (17.9% vs. 12%).

The independent Predictors of AKI in the study population include chronic kidney disease (aOR: 5.3, CI: 5.1-5.5, p ≤ 0.001), cardiogenic shock (aOR: 4.9, CI: 4.7-5.0, p<0.001), cardiac arrest (aOR: 3.1, CI: 2.9-3.3, p<0.001), Charlson comorbidity index of ≥ 3 (aOR: 2.3, CI 2.2-2.4, p<0.001) and Heart Failure (aOR:2.1, CI 2.0-2.1, p<0.001). A diagnosis of AKI was associated with higher in-hospital mortality rates (adjusted odds ratio (aOR): 2.84, CI: 2.7- 3.02, p<0.001), longer mean length of stay (7.1 days vs. 3.0 days, p<0.001) and higher hospital costs ($183,785.8 vs. $101,291.2, p<0.0001). We noted an increasing trend in the percentage of patients who had an AKI, from about 13.5% in 2016 to 16.5% in 2020.

Conclusion

The burden of acute kidney injury following PCI for acute myocardial infarction is significant, with up to 15% of patients developing AKI following the procedure. It is associated with higher in-hospital mortality, longer mean lengths of stay and higher and higher hospitalization costs. Our retrospective study has shown that independent predictors for AKI following PCI include CKD, cardiogenic shock, cardiac arrest, heart failure, Charlson comorbidity index ≥ 3, closer attention and monitoring needs to be paid to patients with these risk factors in order to prevent AKI. We also found an increasing tending in the percentage of those that developed AKI from 2016-2020 underscoring the need for increased preventative measures.

Conflict of Interest

All authors declare that they have no competing interests (financial and non-financial).

Ethics Declaration

The study was not submitted for research ethics approval as the activities described were conducted as part of the Nationwide Inpatient Sample Database (NIS), which is part of the family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP) and uses de-identified data collected from hospitalized patients. Consent was not obtained, given the use of a de-identified database. All the experiments in our study were under the guidelines and agreement regulations of the Agency Healthcare Research and Quality (AHRQ).

Author's Contribution

Akanimo Antia contributed to the conception and design of the research; Chinwendu Angel Onuegbu contributed to the design of the research; Olanrewaju Adabale contributed to the acquisition and analysis of the data; Ovie Okorare contributed to the interpretation of the data; Daniel Ubokudom, Emmanuel Daniel and Endurance Evbayekha helped draft the manuscript. Kenneth Ong made the final reviews. All authors critically revised the manuscript, agreed to be fully accountable for ensuring the integrity and accuracy of the work, and read and approved the final manuscript.

Cosent for Publication

Not applicable. All data using the National Inpatient Sample Database is de-identified.

Funding

None

Data and Materials Availability

The datasets generated and analyzed during the current study are available in the Healthcare Cost and Utilization Project National Data Registry. This Data Use Agreement (“Agreement”) governs the disclosure and use of data in the HCUP Nationwide Databases from the Healthcare Cost and Utilization Project (HCUP), which the Agency maintains for Healthcare Research and Quality (AHRQ). Accordingly, HCUP Databases may only be released in “limited data set” form, as the Privacy Rule defines that term, 45 C.F.R. § 164.514(e). In addition, AHRQ classifies HCUP data as protected health information under the HIPAA Privacy Rule, 45 C.F.R. § 160.103. The datasets generated and analyzed during the current study are not publicly available except for the corresponding author who purchased the data and signed the HCUP data use agreement training. Researchers should readily be able to publicly purchase the same databases we did to conduct research.

References

Author Info

Akanimo Antia1*, Daniel Ubokudom2, Olanrewaju Adabale3, Ovie Okorare4, Emmanuel Daniel5, Endurance Evbayekha6, Chinwendu Angel Onuegbu7 and Kenneth Ong8
 
1Department of Medicine, Lincoln Medical Center, New York, USA
2Department of Medicine, Thomas Hospital, Fairhope, USA
3Department of Medicine, East Carolina University Health Medical Center, Greenville, USA
4Department of Medicine, Vassar Brothers Medical Center, New York, USA
5Department of Medicine, Trinity Health Ann Arbor, Ypsilanti, USA
6Department of Medicine, St. Luke’s Hospital, St. Louis, USA
7Department of Medicine, Montefiore Medical Center, New York, USA
8Department of Cardiovascular Medicine, Lincoln Medical Center, New York, USA
 

Citation: Antia A, Ubokudom D, Adabale O, Okorare O, Daniel E, Evbayekha E, et al (2023) Trends, In-Hospital Outcomes, and Independent Predictors of Acute Kidney Injury in Patients Admitted for the Management of Myocardial Infarction with Percutaneous Coronary Intervention: An Insight from the National Inpatient Sample Database. J Clin Exp Cardiolog.14:819.

Received: 24-Jul-2023, Manuscript No. JCEC-23-25803; Editor assigned: 26-Jul-2023, Pre QC No. JCEC-23-25803 (PQ); Reviewed: 09-Aug-2023, QC No. JCEC-23-25803; Revised: 16-Aug-2023, Manuscript No. JCEC-23-25803 (R); Published: 23-Aug-2023 , DOI: 10.35248/2155-9880.23.14.819

Copyright: © 2023 Antia A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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