Data source
The French hospital discharge abstract database (Programme de Médicalisation des Systèmes d’Informations (PMSI)) contains nationwide data on hospitalizations. Medical and administrative data are systematically collected at each hospital admission (public or private), and each patient is identified with a unique anonymous code. The PMSI data consists of primary and associated diagnoses during hospitalization encoded using WHO International Classification of Diseases and Related Health Problems, 10th revision (ICD-10), and procedures performed during all hospital stays using the French common classification system for medical procedures (Classification commune des actes médicaux). Hospital activity and funding is estimated from PMSI data, thus ensuring the exhaustivity of this database. These hospital data have been used in medical research for about 20 years, and their quality has been confirmed in recent studies [19,20,21,22,23]. This study was approved by the National Committee for Data Protection (declaration of conformity to the methodology of reference 05 obtained on 7/08/2018 under the number 2,204,633 v0) and was conducted in accordance with the Declaration of Helsinki.
Population
This study was a retrospective multicenter study based on nationwide PMSI data. We included all patients aged ≥18 years admitted to all French acute care hospitals (public or private) over a period of 5 years (2012-2016), with a main diagnosis of heart failure (ICD-10 code I50). To include only incident cases, patients with an ICD-10 code I50 of heart failure (main or associated diagnosis) in the previous 4 years were excluded.
Outcomes and follow-up
The main outcome was hospital mortality occurring 1 to 4 years after the first CHF diagnosis. Four-year follow-up on mortality was possible for patients included in 2012 and followed until 2016 (end of our study). The cohort followed-up for 3 years was constituted of patients included in 2013 (followed-up until 2016) or 2012 (followed-up until 2015). By the same logic, 2-year follow-up was possible for patients included in 2014, 2013 or 2012, and 1-year follow-up was possible for patients included in 2015, 2014, 2013 or 2012.
Variables studied
Variables of interest
Our primary objective was to determine if nutritional status had an influence on the mortality of patients diagnosed with heart failure, and to quantify this influence after correcting for confounding factors. We thus divided our population into 4 groups with differing nutritional statuses: (1) a control group of patients who were neither obese nor malnourished, (2) an obesity group, (3) a malnutrition group, and (4) an obesity-malnutrition group.
Malnutrition was identified using ICD-10 codes from the hospital stay for CHF. The diagnosis of malnutrition was based on the presence of at least one of the following in patients under 70 years of age: weight loss ≥10% compared to a prior value (or 5% in 1 month); BMI ≤17 kg/m²; albumin < 30 g/L or prealbumin < 110 mg/l (if no inflammatory syndrome). For patients aged 70 years and older, one of the following criteria was required: weight loss ≥5% in 1 month, or ≥10% in 6 months; BMI < 21 kg/m2; albumin level < 35 g/l. We also classified malnutrition according to severity: severe malnutrition (E40 to E43), moderate malnutrition (E440), slight malnutrition (E441) and malnutrition not otherwise specified (E46). The use of these codes is based on different criteria and coding rules (Supplementary Text 1).
We used a similar procedure to identify obesity, using ICD-10 codes E66 and excluding overweight-specific codes (E6603, E6613, E6683, E6693). The diagnosis of obesity was based on a BMI ≥ 30 kg/m2. Obesity was also classified according to severity: massive obesity (BMI ≥ 50 kg/m2), morbid obesity (BMI 40 to 49 kg/m2), standard obesity (BMI 30 to 39 kg/m2), and obesity not otherwise specified. The classification of the ICD-10 codes used is given in Supplementary Table 1.
Both obesity and malnutrition were recovered at baseline.
Mortality
Mortality was established according to the variables available in the national medical-administrative database (PMSI). This information is contained in the variable “mode of discharge” (alive or deceased).
Confounding variables
The patient characteristics considered as confounding variables, including age, gender, the etiology of CHF (Ischemic, dilated or hypertensive cardiomyopathy), the presence of comorbidities such as diabetes, hypertension, dyslipidemia, kidney failure, chronic obstructive pulmonary disease (COPD), and the presence of infection and shock during the hospital stay, were added to our model in order to perform a statistical adjustment. These confounding variables were identified as mortality risk factors in a previous study [24]. A complete list of the confounding variables is provided in Supplementary Table 2.
All of these confounding variables were recovered at baseline.
Statistical analysis
The different variables studied were compared using the Chi-2 test or Fisher’s exact test for categorical data and Student’s t-test or Mann-Whitney test for continuous data.
To study the influence of the variables of interest defined above (all dichotomized) on mortality on a follow up period ranging from 1 to 4 years, we first used a Kaplan-Meier method.
To take into account other factors, we then performed a survival analysis using a Cox proportional hazards regression model. The proportional hazard assumption was studied using the Kaplan-Meir curves, and interactions with time were taken into account when this assumption was violated. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated after adjustment for potential confounders, using time from index hospitalization for CHF to in-hospital death. Outcome was measured over a 4-year period after index hospitalization for CHF. Individuals were censored at death, or the latest all-cause hospitalization for those who did not die. We followed individuals until in-hospital death, or the end of the 4-year follow-up period.
A sensitivity analysis was performed by limiting the follow-up to one year in order to have a larger number of incident cases and therefore a higher statistical power.
Another sensitivity analysis was conducted by excluding ICD-10 codes E660 (Obesity due to excess calories).
The statistical significance threshold was set at < 0.05. All analyses were performed using SAS (SAS Institute Inc, Version 9.4, Cary, NC).