Clinical predictors of length of stay in adults with congenital heart disease

Risk factors of postoperative acute kidney injury in patients with complex congenital heart disease and significance of early detection of serum transcription factor Nkx2.5

OBJECTIVE

This study was designed to investigate the risk factors of postoperative acute kidney injury (AKI) in patients with complex congenital heart disease (CHD) and the significance of early detection of serum transcription factor Nkx2.5.

METHODS

A total of 121 CHD patients admitted to the Shengli Clinical Medical College of Fujian Medical University were selected as study participants, among whom 69 patients with AKI after cardiac surgery were set as the research group (RG), and the rest of the 52 patients without AKI were set as the control group (CG). Cardiopulmonary bypass (CPB) duration, aortic occlusion time, postoperative creatinine (Cr) level and mechanical ventilation (MV) time were compared between the two groups. The expression and clinical significance of Nkx2.5 in the two groups were detected. Intensive Care Unit (ICU) residence time and total hospital stay were compared, and the risk factors were analyzed.

RESULTS

The RG presented remarkably longer CPB duration and aortic occlusion time, evidently higher postoperative Cr level and longer MV time, and observably lower Nkx2.5 level in comparison to the CG (all P<0.05). According to the analysis of receiver operating characteristic (ROC) curves, Nkx2.5 displayed a favorable diagnostic value in predicting the occurrence of CHD complicated with AKI. ICU residence time and total hospital stay were longer in the RG than in the CG (P<0.05). CPB time and aortic occlusion time were independent risk factors for AKI in CHD patients, while surgical methods and Nkx2.5 detection were independent protective factors (P<0.05).

CONCLUSIONS

CPB time, aortic occlusion time and surgical methods, as well as Nkx2.5 detection are independent factors affecting AKI in patients with CHD. Early detection of serum transcription factor Nkx2.5 is of particular importance for clinical diagnosis of CHD patients complicated with AKI.

Health Service Utilization Patterns Among Adults With Congenital Heart Disease: A Population-Based Study

Background

Several studies have examined hospitalizations among patients with adult congenital heart disease (ACHD). Few investigated other services or utilization patterns. Our aim was to study service utilization patterns and predictors among patients with ACHD.

Methods and Results

We identified 11 653 patients with ACHD aged ≥18 years (median, 47 years), through electronic records of 2 large Israeli healthcare providers (2007–2011). The association between patient, disease, and sociogeographic characteristics and healthcare resource utilization were modeled as recurrent events accounting for the competing death risk. Patients with ACHD had high healthcare utilization rates compared with the general population. The highest standardized service utilization ratios (SSRs) were found among patients with complex congenital heart disease including primary care visits (SSR, 1.53; 95% CI, 1.47–1.58), cardiology outpatient visits (SSR, 5.17; 95% CI, 4.69–5.64), hospitalizations (SSR, 6.68; 95% CI, 5.82–7.54), and days in hospital (SSR, 15.37; 95% CI, 14.61–16.12). Adjusted resource utilization hazard increased with increasing lesion complexity. Hazard ratios (HRs) for complex versus simple disease were: primary care (HR, 1.14; 95% CI, 1.06–1.23); cardiology outpatient visits (HR, 1.40; 95% CI, 1.24–1.59); emergency department visits (HR, 1.19; 95% CI, 1.02–1.39); and hospitalizations (HR, 1.75; 95% CI, 1.49–2.05). Effects attenuated with age for cardiology outpatient visits and hospitalizations and increased for emergency department visits. Female sex, geographic periphery, and ethnic minority were associated with more primary care visits, and female sex (HR versus men, 0.89 [95% CI, 0.84–0.94]) and periphery (HR, 0.72 [95% CI, 0.58–0.90] for very peripheral versus very central) were associated with fewer cardiology visits. Arab minority patients also had high hospitalization rates compared with the majority group of Jewish or other patients.

Conclusions

Healthcare utilization rates were high among patients with ACHD. Female sex, geographic periphery, and ethnicity were associated with less optimal service utilization patterns. Further research should examine strategies to optimize service utilization in these groups.

Hospitalizations in adult patients with congenital heart disease: an emerging challenge

The vast majority of patients with congenital heart disease (CHD) survive into adulthood, but many face lifelong complications, which often result in a hospital admission. The increasing number of hospitalizations in adults with CHD (ACHD) poses a significant challenge for healthcare systems globally, especially as heart failure (HF) is becoming increasingly common in this population and is the leading cause of morbidity and mortality. Besides HF, other major contributors to this increase in admission volume are hospitalizations related to mild lesions, comorbidities and pregnancies. Ιn-hospital mortality ranges between 0.8 and 6.1%, while hospitalizations related to HF predict medium-term mortality in ACHD population. Understanding the predictors of hospitalization and in-hospital mortality is, therefore, important for ACHD healthcare providers, who should identify patients at risk that require escalation of treatment and/or close monitoring. This article reviews the available literature on hospitalization patterns in ACHD patients, with a focus on HF-related hospital admissions and specific diagnostic subgroups.

Predicting 30-day readmission after congenital heart surgery across the lifespan

INTRODUCTION

Hospital readmission is an important driver of costs among patients with CHD. We assessed predictors of 30-day rehospitalisation following cardiac surgery in CHD patients across the lifespan.

METHODS

This was a retrospective analysis of 981 patients with CHD who had cardiac surgery between January 2011 and December 2012. A multivariate logistic regression model was used to identify demographic, clinical, and surgical predictors of 30-day readmission. Receiver operating curves derived from multivariate logistic modelling were utilised to discriminate between patients who were readmitted and not-readmitted at 30 days. Model goodness of fit was assessed using the Hosmer-Lemeshow test statistic.

RESULTS

Readmission in the 30 days following congenital heart surgery is common (14.0%). Among 981 patients risk factors associated with increased odds of 30-day readmission after congenital heart surgery through multivariate analysis included a history of previous cardiac surgery (p < 0.001), longer post-operative length of stay (p < 0.001), as well as nutritional (p < 0.001), haematologic (p < 0.02), and endocrine (p = 0.04) co-morbidities. Patients who underwent septal defect repair had reduced odds of readmission (p < 0.001), as did children (p = 0.04) and adult (p = 0.005) patients relative to neonates.

CONCLUSION

Risk factors for readmission include a history of cardiac surgery, longer length of stay, and co-morbid conditions. This information may serve to guide efforts to prevent readmission and inform resource allocation in the transition of care to the outpatient setting. This study also demonstrated the feasibility of linking a national subspecialty registry to a clinical and administrative data repository to follow longitudinal outcomes of interest.

Prolonged length of stay after surgery for adult congenital heart disease: a single-centre study in a developing country

BACKGROUND

With the growing number of adults requiring operations for CHD, prolonged length of stay adds an additional burden on healthcare systems, especially in developing countries. This study aimed to identify factors associated with prolonged length of stay in adult patients undergoing operations for CHD.

METHODS

This retrospective study included all adult patients (≥18 years) who underwent cardiac surgery with cardiopulmonary bypass for their CHD from 2011 to 2016 at a tertiary-care private hospital in Pakistan. Prolonged length of stay was defined as hospital stay >75th percentile of the overall cohort (>8 days).

RESULTS

This study included 166 patients (53.6% males) with a mean age of 32.05 ± 12.11 years. Comorbid disease was present in 59.0% of patients. Most patients underwent atrial septal defect repair (42.2%). A total of 38 (22.9%) patients had a prolonged length of stay. Post-operative complications occurred in 38.6% of patients. Multivariable analysis showed that pre-operative body mass index (odds ratio: 0.779; 95% confidence interval: 0.620-0.980), intraoperative aortic cross-clamp time (odds ratio: 1.035; 95% confidence interval: 1.009-1.062), and post-operative acute kidney injury (odds ratio: 7.392; 95% confidence interval: 1.036-52.755) were associated with prolonged length of stay.

CONCLUSION

Predictors of prolonged length of stay include lower body mass index, longer aortic cross-clamp time, and development of post-operative acute kidney injury. Shorter operations, improved pre-operative nutritional optimisation, and timely management of post-operative complications could help prevent prolonged length of stay in patients undergoing operations for adult CHD.

Predictors of in-hospital length of stay among cardiac patients: A machine learning approach

OBJECTIVE

The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages. This will affect healthcare systems especially with the current situation of decreased bed capacity and increasing costs. Therefore, accurately predicting LOS would have a positive impact on healthcare metrics. The aim of this study is to develop a machine learning-based model approach for predicting in-hospital LOS for cardiac patients.

DESIGN

Using electronic medical records, we retrospectively extracted all records of patients' visits that were admitted under adult cardiology service. Admission diagnosis and primary treating physician were reviewed to verify selection criteria. A predictive machine learning-based model approach was applied to incorporate simple baseline health data at admission time to predict LOS. Patients were divided into three groups based on their LOS: short (<3 days), intermediate (3-5 days) and long (>5 days). Information gain algorithm was utilized to select the most relevant attributes. Only attributes with information gain of more than zero were used in model building. Four different machine learning techniques were evaluated and their diagnostic accuracy measures were compared.

SETTING

The dataset of this study included adult patients who were admitted between 2008 and 2016 in King Abdulaziz Cardiac Center (KACC). The center is located in King Abdulaziz Medical City Complex in Riyadh, the capital of Saudi Arabia.

PARTICIPANTS (DATASET)

A total of 16,414 consecutive inpatient visits for 12,769 unique patients (mean age of 58.8 ± 16 years of which 68.2% were males) between 2008 and 2016 were included. The study cohort had a high prevalence of cardiovascular risk factors (hypertension 56%, diabetes 56%, dyslipidemia 52%, obesity 33% and smoking 24%). The most common admitting diagnosis was acute coronary syndrome (36%).

RESULTS

The variables with highest impact on the prediction of in-hospital LOS were on admission heart rate, on admission systolic and diastolic blood pressure, age and insurance status (eligibility). Using machine learning models; Random Forest (RF) model outperformed among all other models (sensitivity (0.80), accuracy (0.80), and AUROC (0.94)).

CONCLUSION

We showed that machine learning methods provide accurate prediction of LOS for cardiac patients. This is can be used in clinical bed management and resources allocation.

Accuracy of claim data in the identification and classification of adults with congenital heart diseases in electronic medical records

BACKGROUND

The content of electronic medical records (EMRs) encompasses both structured data, such as billing codes, and unstructured data, including free-text reports. Epidemiological and clinical research into adult congenital heart disease (ACHD) increasingly relies on administrative claim data using the International Classification of Diseases (9th revision) (ICD-9). In France, administrative databases use ICD-10, the reliability of which is largely unknown in this context.

AIMS

To assess the accuracy of ICD-10 codes retrieved from administrative claim data in the identification and classification of ACHD.

METHODS

We randomly included 6000 patients hospitalized at least once in 2000-2014 in a cardiology department with a dedicated specialized ACHD Unit. For each patient, the clinical diagnosis extracted from the EMR was compared with the assigned ICD-10 codes. Performance of ICD-10 codes in the identification and classification of ACHD was assessed by estimating sensitivity, specificity and positive predictive value.

RESULTS

Among the 6000 patients included, 780 (13%) patients with ACHD were manually identified from EMRs (107,092 documents). ICD-10 codes correctly categorized 629 as having ACHD (sensitivity 0.81, 95% confidence interval 0.78-0.83), with a specificity of 0.99 (95% confidence interval 0.99-1). The performance of ICD-10 codes in correctly categorizing the ACHD defect subtype depended on the defect, with sensitivity ranging from 0 (e.g. unspecified congenital malformation of tricuspid valve) to 1 (e.g. common arterial trunk), and specificity ranging from 0.99 to 1.

CONCLUSIONS

Administrative data using ICD-10 codes is a precise tool for detecting ACHD, and may be used to establish a national cohort. Mining free-text reports in addition to coded administrative data may offset the lack of sensitivity and accuracy when describing the spectrum of congenital heart disease using ICD-10 codes.

Impact of Non-cardiac Comorbidities in Adults with Congenital Heart Disease: Management of Multisystem Complications

The prevalence and impact of non-cardiac comorbidities in adult patients with congenital heart disease increase over time, and these complications are often specifically a consequence of the long-term altered cardiovascular physiology or sequelae of previous therapies. For the ACHD patient admitted to the intensive care unit (ICU) for either surgical or medical treatment, an assessment of the burden of multisystem disease, as well as an understanding of the underlying cardiovascular pathophysiology, is essential for optimal management of these complex patients. This chapter takes an organ-system-based approach to reviewing common comorbidities in the ACHD patient, focusing on conditions that are directly related to ACHD status and may significantly impact ICU care.