Detailed Description of Data Source
The database includes basic admission and demographic information (not all of
which were used in the present study), vital signs, laboratory and radiology
results, medications, discharge diagnoses, nursing notes, physician discharge
summaries and dates of death. MIMIC-II contains patients from five ICU types:
medical (MICU), surgical (SICU), cardiac (CCU), cardiac surgery recovery (CSRU)
and neonatal (NICU). Other clinical data were added to the database including
pharmacy, provider order entry records, admission and death records, discharge
summaries and 9th Revision (ICD-9) codes.
All data were extracted from the MIMIC-II database (v2.6), including
demographic information (e.g., age and gender), clinical information from
admission notes (diuretic use), laboratory results (e.g., blood gas analysis,
minimum serum chloride, minimum serum potassium, maximum serum sodium,
serum creatinine and urine chloride levels), pharmacy information (e.g., diuretic
and alkali administration) and discharge diagnoses according to ICD-9 codes. In
addition, cumulative gastric output fluids during the ICU stay were collected, and
patients above the 90th percentile were considered to have high gastric output.
The MIMIC-II database also includes the severity of illness as assessed
through the simplified acute physiology score (SAPS I) and Sequential Organ
Failure Assessment (SOFA) score [1],[2] .
Complete Statistical Analysis
First, we used descriptive statistics, including the mean with standard
deviation, median and interquartile range or frequencies, to describe the
population as appropriate. Next, we investigated the association between the
maximum serum bicarbonate level as a continuous variable and in-hospital
mortality. For the univariate analysis, we used chi-square tests, t-tests, KruskalWallis tests, one-way analysis of variance and Mann-Whitney U tests to evaluate
statistical significance. All tests were two-sided, and a p-value of < 0.05 was
considered significant.
To obtain multivariable models, we first used multiple imputations (5
replications) to create complete data sets because up to 3% of observations were
missing values for Na+, K+ and Cl-. We performed several logistic regression
analyses to determine the association between increased serum bicarbonate and
its duration and hospital mortality. The robustness of the association between
serum bicarbonate and clinical outcome was tested by progressively adding
variables to the models that might have an effect on serum bicarbonate or the
clinical outcome considered. The following covariates were included in the
models: age, gender, SOFA and SAPS-I scores, type of ICU admission (medical or
surgical), minimum and maximum serum bicarbonate levels, acute kidney injury
(AKI) severity, hyponatremia and the main comorbidity groups obtained from
ICD-9–Clinical Modification codes using the Elixhauser comorbidity index. Other
factors associated with high serum bicarbonate levels in univariate analysis also
included hypokalemia, hypernatremia, gastric output and renal replacement
therapy (RRT), all of which were considered during ICU stays. Based on the
initial exploration of maximum serum bicarbonate levels for hospital mortality,
which revealed a U-shaped association, we assessed a cubic spline regression
model with serum bicarbonate as a continuous variable with two knots at 24 and
31 mEq/L. The values were then categorized as less than 25 or greater than 30
vs. 25-30 mEq/L. This same approach was made using SBE with one knot at 1mEq/L and 5mEq/L. After testing for colinearity, multiple covariate analyses
were applied. For interpretation, we provided odds ratio (OR) values for each 5mEq/L increases or decrease. We also performed bootstrap validation of the
full-adjusted models using the entire cohort with 1,000 resampling runs to
assess bias due to over-fitting. The specific covariates used in each logistic
regression model are specified in the text and/or tables. Serum albumin was not
included in the multivariate analysis because less than 50% of patients had these
measurements available. We performed a subgroup analysis to search for
interactions by each known cause of metabolic alkalosis to investigate the
association between high serum bicarbonate levels and in-hospital mortality.
Because ICU discharge and mortality represent competing risks, we used
the cumulative incidence function to analyze time to hospital discharge and
mortality over 28 days. To investigate whether high serum bicarbonate had any
influence on the impact on long-term survival, we performed a multivariate Cox
regression analysis in all patients discharged alive from the hospital. In Cox
regression analysis, we also adjusted for age, gender, simplified acute physiology
score (SAPS-I), sequential organ failure assessment (SOFA), main comorbidities,
type of admission (clinical or surgical), diuretic use, either previous to or during
ICU stay, alkali administration, hypo/hyperkalemia, hypo/hypernatremia, renal
replacement therapy (RRT) and gastric output, minimum serum bicarbonate,
partial CO2 pressure > 45mmHg, acute kidney injury severity and mechanical
ventilation during ICU stay. All covariates were tested to maintain proportional
hazard assumption3. Statistical analyses were performed using SPSS 19.0 for
Windows.
Arterial Blood Sample Analysis – Sensitivity Analysis
In a sensitivity analysis, we attempted to separately evaluate patients
with arterial blood gas measurements (n = 12,696). In these patients a standard
base excess (SBE) greater than 5 mEq/L (n = 3,273) was used to define metabolic
alkalosis instead of serum bicarbonate levels. The majority of patients with high
SBE (72.2%) also had alkalemia (pH > 7.45). In these subset analyses, metabolic
alkalosis remained independently associated with the death rate in uni- and
multivariate analyses (see supplementary table 4).
References
1. Le Gall JR, Loirat P, Alperovitch A, Glaser P, Granthil C, Mathieu D, Mercier P,
Thomas R, Villers D. (1984) A simplified acute physiology score for ICU patients.
Crit Care Med 12:975-977.
2. Vincent JL, de Mendonça A, Cantraine F, Moreno R, Takala J, Suter PM, Sprung
CL, Colardyn F, Blecher S. (1998) Use of the SOFA score to assess the incidence of
organ dysfunction/failure in intensive care units: results of a multicenter,
prospective study. Working group on "sepsis-related problems" of the European
Society of Intensive Care Medicine. Crit Care Med 26:1793-1800.