4 Bacteremia

5 Introduction to Bacteremia

To demonstrate the workflow and content of IDA, we created a hypothetical research aim and corresponding statistical analysis plan, which is described in more detail in the section Bact_SAP.Rmd.

Hypothetical research aim for IDA is to develop a multivariable diagnostic model for bacteremia using 49 continuous laboratory blood parameters, age and gender with the primary aim of prediction and a secondary aim of describing the association of each variable with the outcome (‘explaining’ the multivariable model).

A diagnostic prediction model was developed based on this data set and validated in “A Risk Prediction Model for Screening Bacteremic Patients: A Cross Sectional Study” Ratzinger et al, PLoS One 2014. The assumed research aim is in line with this diagnostic prediction model.

5.1 Dataset Description

Ratzinger et al (2014) performed a diagnostic study in which age, sex and 49 laboratory variables can be used to diagnose bacteremia status of a blood sample using a multivariable model. Between January 2006 and December 2010, patients with the clinical suspicion to suffer from bacteraemia were included if blood culture analysis was requested by the responsible physician and blood was sampled for assessment of haematology and biochemistry. The data consists of 14,691 observations from different patients.

Our version of this data was slightly modified compared to original version, and this modified version was cleared by the Medical University of Vienna for public use (DC 2019-0054). Variable names have been kept as they were (partly German abbreviations). A data dictionary is available in the misc folder of the project directory (‘bacteremia-DataDictionary.csv’).

In the original paper describing the study (Ratzinger et al, PLoS One 2014), a machine learning approach was taken to diagnose a positive status of blood culture. The true status was determined for all blood samples by blood culture analysis, which is the gold standard. Here we will make use of a multivariable logistic regression model.

5.2 Bacteremia dataset contents

5.2.1 Source dataset

We refer to the source data set as the dataset available in this repository.

Display the source dataset contents. This dataset is in the data folder of the project directory.

Data frame:bact

14691 observations and 53 variables, maximum # NAs:7114

Name	Storage	NAs
ID	integer	0
SEX	integer	0
AGE	integer	0
MCV	double	42
HGB	double	41
HCT	double	42
PLT	integer	42
MCH	double	42
MCHC	double	42
RDW	double	56
MPV	double	702
LYM	double	262
MONO	double	246
EOS	double	135
BASO	double	146
NT	integer	2467
APTT	double	2549
FIB	integer	2567
SODIUM	integer	1282
POTASS	double	2008
CA	double	1276
PHOS	double	1242
MG	double	1869
CREA	double	159
BUN	double	172
HS	double	3061
GBIL	double	1441
TP	double	1583
ALB	double	1676
AMY	integer	3913
PAMY	integer	7114
LIP	integer	3699
CHE	double	2447
AP	integer	1400
ASAT	integer	1154
ALAT	integer	987
GGT	integer	1262
LDH	integer	1714
CK	integer	2080
GLU	integer	4192
TRIG	integer	5061
CHOL	integer	5045
CRP	double	155
BASOR	double	732
EOSR	double	732
LYMR	double	732
MONOR	double	732
NEU	double	728
NEUR	double	732
PDW	double	1102
RBC	double	461
WBC	double	462
BloodCulture	character	0

5.2.2 Updated analysis dataset

Additional meta-data is added to the original source data set. We write this new modified (annotated) data set back to the data folder after adding additional meta-data for all variables. The meta-data is taken from the data dictionary.

At the stage we could select the variables of interest to take in to the IDA phase by dropping variables we do not check in IDA.

As a cross check we display the contents again to ensure the additional data is added, and then write the changes to the data folder in the file “data/a_bact.rda”.

Input object size: 5119632 bytes; 53 variables 14691 observations New object size: 5159904 bytes; 53 variables 14691 observations Input object size: 5277552 bytes; 54 variables 14691 observations New object size: 5219544 bytes; 54 variables 14691 observations ::: {.cell-output-display}

Data frame:a_bact

14691 observations and 54 variables, maximum # NAs:7114

Name	Labels	Units	Class	Storage	NAs
ID	Patient Identification	1-14691	integer	integer	0
SEX	Patient sex	1=male, 2=female	integer	integer	0
AGE	Patient Age	years	integer	integer	0
MCV	Mean corpuscular volume	pg	numeric	double	42
HGB	Haemoglobin	G/L	numeric	double	41
HCT	Haematocrit	%	numeric	double	42
PLT	Blood platelets	G/L	integer	integer	42
MCH	Mean corpuscular hemoglobin	fl	numeric	double	42
MCHC	Mean corpuscular hemoglobin concentration	g/dl	numeric	double	42
RDW	Red blood cell distribution width	%	numeric	double	56
MPV	Mean platelet volume	fl	numeric	double	702
LYM	Lymphocytes	G/L	numeric	double	262
MONO	Monocytes	G/L	numeric	double	246
EOS	Eosinophils	G/L	numeric	double	135
BASO	Basophiles	G/L	numeric	double	146
NT	Normotest	%	integer	integer	2467
APTT	Activated partial thromboplastin time	sec	numeric	double	2549
FIB	Fibrinogen	mg/dl	integer	integer	2567
SODIUM	Sodium	mmol/L	integer	integer	1282
POTASS	Potassium	mmol/L	numeric	double	2008
CA	Calcium	mmol/L	numeric	double	1276
PHOS	Phosphate	mmol/L	numeric	double	1242
MG	Magnesium	mmol/L	numeric	double	1869
CREA	Creatinine	mg/dl	numeric	double	159
BUN	Blood urea nitrogen	mg/dl	numeric	double	172
HS	Uric acid	mg/dl	numeric	double	3061
GBIL	Bilirubin	mg/dl	numeric	double	1441
TP	Total protein	G/L	numeric	double	1583
ALB	Albumin	G/L	numeric	double	1676
AMY	Amylase	U/L	integer	integer	3913
PAMY	Pancreas amylase	U/L	integer	integer	7114
LIP	Lipases	U/L	integer	integer	3699
CHE	Cholinesterase	kU/L	numeric	double	2447
AP	Alkaline phosphatase	U/L	integer	integer	1400
ASAT	Aspartate transaminase	U/L	integer	integer	1154
ALAT	Alanin transaminase	U/L	integer	integer	987
GGT	Gamma-glutamyl transpeptidase	G/L	integer	integer	1262
LDH	Lactate dehydrogenase	U/L	integer	integer	1714
CK	Creatinine kinases	U/L	integer	integer	2080
GLU	Glucoses	mg/dl	integer	integer	4192
TRIG	Triclyceride	mg/dl	integer	integer	5061
CHOL	Cholesterol	mg/dl	integer	integer	5045
CRP	C-reactive protein	mg/dl	numeric	double	155
BASOR	Basophile ratio	%	numeric	double	732
EOSR	Eosinophil ratio	%	numeric	double	732
LYMR	Lymphocyte ratio	% (mg/dl)	numeric	double	732
MONOR	Monocyte ratio	%	numeric	double	732
NEU	Neutrophiles	G/L	numeric	double	728
NEUR	Neutrophile ratio	%	numeric	double	732
PDW	Platelet distribution width	%	numeric	double	1102
RBC	Red blood count	T/L	numeric	double	461
WBC	White blood count	G/L	numeric	double	462
BloodCulture	Blood culture result for bacteremia	no, yes	character	character	0
BC	bacteremia	0/1	integer	integer	0

:::

5.3 Section session info

R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] Hmisc_4.6-0     Formula_1.2-4   survival_3.2-13 lattice_0.20-45
 [5] forcats_0.5.1   stringr_1.4.0   dplyr_1.0.9     purrr_0.3.4    
 [9] readr_2.1.1     tidyr_1.2.0     tibble_3.1.7    ggplot2_3.3.6  
[13] tidyverse_1.3.1 here_1.0.1     

loaded via a namespace (and not attached):
 [1] httr_1.4.2          jsonlite_1.7.2      splines_4.1.2      
 [4] modelr_0.1.8        assertthat_0.2.1    latticeExtra_0.6-29
 [7] renv_0.15.5         cellranger_1.1.0    pillar_1.7.0       
[10] backports_1.4.1     glue_1.6.2          digest_0.6.29      
[13] checkmate_2.1.0     RColorBrewer_1.1-3  rvest_1.0.2        
[16] colorspace_2.0-3    htmltools_0.5.2     Matrix_1.3-4       
[19] pkgconfig_2.0.3     broom_0.8.0         haven_2.4.3        
[22] scales_1.2.0        jpeg_0.1-9          tzdb_0.2.0         
[25] htmlTable_2.3.0     generics_0.1.2      ellipsis_0.3.2     
[28] withr_2.5.0         nnet_7.3-16         cli_3.3.0          
[31] magrittr_2.0.3      crayon_1.5.1        readxl_1.3.1       
[34] evaluate_0.14       fs_1.5.2            fansi_1.0.3        
[37] xml2_1.3.3          foreign_0.8-81      data.table_1.14.2  
[40] tools_4.1.2         hms_1.1.1           lifecycle_1.0.1    
[43] munsell_0.5.0       reprex_2.0.1        cluster_2.1.2      
[46] compiler_4.1.2      rlang_1.0.3         grid_4.1.2         
[49] rstudioapi_0.13     htmlwidgets_1.5.4   base64enc_0.1-3    
[52] rmarkdown_2.11      gtable_0.3.0        DBI_1.1.2          
[55] R6_2.5.1            gridExtra_2.3       lubridate_1.8.0    
[58] knitr_1.37          fastmap_1.1.0       utf8_1.2.2         
[61] rprojroot_2.0.2     stringi_1.7.6       Rcpp_1.0.8.3       
[64] vctrs_0.4.1         rpart_4.1-15        png_0.1-7          
[67] dbplyr_2.1.1        tidyselect_1.1.2    xfun_0.31