Author: david

Narrative Medicine Category, aka the human side

Mountains beyond mountains (Tracy Kidder)

• Journey of Paul Farmer of Boston
• Improve global health in Haiti and beyond

When breath becomes air (Paul Kalanithi)

• Neurosurgeon becomes a patient himself

Attitude and Learning

An Astronauts Guide to Life on Earth (Chris Hadfield)

• How to be net zero in a team
• Excellence through life long learning
• Simulations & preparation for high-stakes procedures
• What is the next thing that is going to kill me?

Mastery (Rob Greene)

• How to become a master in any field
• Three phases: apprenticeship, creative exploration, mastery

Streetlight and Shadows (Gary Klein)

• Expertise vs learning protocols and procedures
• Decision making in ordered vs complex environments

Mistakes were made, but not by me (Carol Travis)

• Cognitive dissonance
• Confirmation bias
• Making mistakes in a healthy way

Peak (Anders Ericsson)

• How to become a master
• Naive vs deliberate practice

Effective work

Deep work (Cal Newport)

• To work effectively you need to do away with distractions
• Work smart vs work hard
• Note this also applies to personal life (his book Digital Minimalism)

Seven habits of highly successful people (Steven Covey)

• General setup: be a healthy you, engage with others
• Big picture thinking

The five dysfunctions of a team (Patrick Lencioni)

• why teams struggle

Finally my current reading list

• Field Guide to Understanding Human Error (Sidney Dekker)
• Deadly Medicines and Organised Crime (Peter Gotzsche)
• What Doctors Feel (Danielle Ofri)

• Use lintr in R studio via Marker pane / Diagnostics
• Write code in Rmarkdown, adding a few notes at the end on reproducibility of the analysis
• Use Git: local data (proprietary/sensitive), cloud code
• Use modular code
• Comment your code
• Don’t Repeat Yourself (DRY)
• Be concise, clear and consistent
• Style Guide: Hadley and Google

I wrote this short reference. The first 3 operators exist thanks to the magrittr package, the last is implemented in ggplot2.

The %>% operator (the pipe)

head(data)
data %>% head()

The %$% operator

cor.test(data$var1, data$var2)
data %$% cor.test(var1,var2)

The %<>% operator

var = var %>% sqrt()
var %<>% sqrt()

The %+% operator

p = data %>% ggplot(aes(x,y)) + geom_point()
p = subset(data, complete == TRUE) %>% ggplot(aes(x,y)) + geom_point()

p %+% subset(data, complete == TRUE)

A brief note to some give space to a few valuable, recent articles on this topic:

• Measurement error and the replication crisis, a perspective article in Science
• Problems with small sample sizes, a blog post
• Low statistical power in biomedical science: a review of three human research domains, published in the Royal Society Open Science journal
• Acknowledging and Overcoming Nonreproducibility in Basic and Preclinical Research, published in JAMA

Obviously I was excited about ESPGI 2016, both because my former supervisor Agneta Wikman organised the meeting and because I got to meet my upcoming colleagues from Sanquin Amsterdam. It was a small congress and very special to be part of such an intimate community.

The first day of the meeting offered a range of topics around platelets & inflammation, FNAIT, and neutropenia/agranulocytosis. My personal highlights were:

• As evidence suggests that there is regulating crosstalk between platelets and T-cells, it was offered that in ITP, T-cells dysregulation into a T-reg deficient / Th1 promoting phenotype might be due to direct absence of immunomodulation by platelets.
• Co-incubation of NK-cells with human platelets in the presence of HPA-1a antibodies seemed to induce NK-cell-platelet complexes, whereas this was not observed in absence of the antibodies. Further research will need to show platelet activation/clearance and NK-cell cytokine profiles.
• Ulrich Sachs presented exciting data that offered that anti-HPA-1a antibodies in FNAIT specific for alpha-v beta-3 antibodies induced endothelial permeability and disruption. Quantification of specific alpha-v beta-3 Ab offered a high ROC-AUC to identify patients that experienced intracranial hemorrhage (n=17/18 vs n=18 ctrl). Further research will need to show if such classification can be achieved in screened samples, because predictive values and ROC are likely to be overestimated here due to the employed case-control design. The role of thrombocytopenia in driving ICH might be related to a loss of hemostatic functions or competition for antibody-body binding with endothelial cells, and should be further studied.
• An exciting FACS technique was used to study DC in mice for cross-presentation (XCR1+) and non-presenting, tolerizing phenotype (SIRP alpha+), indicating that in a murine ITP model the subset of thymic tolerizing DCs is modulated by both IVIG and splenectomy.

The second day continued with advances on HLA immunology, ITP and TRALI. Major findings were driven by Canada:

• Anne Zufferey showed beautiful data on the endocytosis of exogenous antigen and cross-presentation of these antigens by murine megakaryocytes to CD8+ T-cells.
• Alan Lazarus presented his data on the manipulation of FcyR by an engineered antibody fragment and its potential role in treating ITP
• The mouse-model work of Rick Kapur unravelled IL-10 production from Treg and DC as the key protective factor against TRALI, evoking briefly that IL-10 therapy might rescue early detected TRALI from progression.

It was finally announced that the next ESPGI meeting will be in Amsterdam 2018.

I recently read the Weissgerber et al. paper on PLOS Biology on data presentation. Next to reviewing the data presentation pratices of > 700 papers, the authors make a convincing case of bad data visualisation through examples.

The authors advocate for a new paradigm for data presentation, including more rigorous journal policies and better training of investigators.

Weissgerber et al. made a good point of old news, but they  convey the story based on actual data and beautifully chosen examples.

Reading through their article promted me to go back and collect several earlier valuable resources, presented below. Let’s make better figures!

References

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4): e1002128. doi:10.1371/journal.pbio.1002128 (PLOS online)
Several people commenting on the paper made good work to present similar graphs in R in example 1, example 2  and example 3.

Rougier NP, Droettboom M, Bourne PE (2014) Ten Simple Rules for Better Figures. PLoS Comput Biol 10(9): e1003833. doi:10.1371/journal.pcbi.1003833 (PLOS online)

Top ten worst graphs and some resources (Brohmans website)

A great talk on How to display data badly, PDF slides available

For completeness, the landmark paper here is Wainer H (1984) How to display data badly. The American Statistician 38:137-147 (on JSTOR or google).

Datasets with high dimensions emerge massively in biomedical research. Think of gene expression analyses where the amount of measured variables (e.g. 20 000 genes) exceeds the number of samples (e.g. 100) by a multitude.

Here I put some quality information as a resource.

Principal component analysis

Definition Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components (Wikipedia).

Screenshot from Setosa, see link below.

I can recommend reading this straighforward paper: Ringnér, M. (2008). What is principal component analysis? Nature Biotechnology, 26(3), 303–304. http://doi.org/10.1038/nbt0308-303

Figure from Ringner, see citation above.

Continue reading on PCA

• Setosa: graphical explanation
• PCA for dummies: eigenvectors etc

t-SNE

Definition t-Distributed Stochastic Neighbor Embedding (t-SNE) is a technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets. (Laurens van der Maaten website)

This paper in Nature Immunology beautifully uses this technique to describe and discriminate murine immune cells against a 38-surface antibody mass cytometry assay.

Becher, B., Schlitzer, A., Chen, J., Mair, F., Sumatoh, H. R., Teng, K. W. W., et al. (2014). High-dimensional analysis of the murine myeloid cell system. Nature Immunology, 15(12), 1181–1189. http://doi.org/10.1038/ni.3006

tSNE analysis objectively delineates myeloid cell subsets of lung, spleen and bone marrow. (a–c) tSNE composite dimensions (dim.) 1 and 2 for cells derived from lung (a), spleen (b) and bone marrow (c). Left, cells grouped according to biased (traditional) definitions with gating strategies similar to that described in Figure 1. Remaining cells (gray) are those unaccounted for by these definitions; predominant unidentified clusters are indicated with arrows. Right, cells are grouped according to automatic (unbiased) cluster designation. Predominant clusters (frequencies >1%) including those that correspond to subsets not accounted for by traditional gating are labeled with cluster numbers. Average frequencies (± s.e.m., n = 3 mice) as percentage of total CD45+CD90−CD19−CD3− population are shown for each subset. Alv., alveolar; inter., interstitial; rem., remaining.

Posts in this category follow the NEJM blog case discussion format, yielding dense, useful bedside information about a specific clinical issue.

Case

An 10-year old girl presented with lethargy, abdominal pain and bloody diarrea since 3 days. Her mother noted increasing paleness of the skin. Laboratory testing revealed Coombs-negative hemolytic anemia with fragmentocytes, thrombocytopenia, and elevated creatinin and ureum. A stool culture was found significant for Shigatoxin-producing E. coli (STEC).

About 90% of STEC-hemolytic uremic syndromes occure with prodromal diarrea. STEC laboratory testing may include ELISA against shigatoxin antigens, stool culture with enrichment to promote E. coli O157:H7 strain production, as well as  STEC-serotype specific IgM or anti-LPS serology.

Clinical Pearls

Schistocytes (fragmented RBC), also called helmet cells.

• STEC-HUS is presenting as multi-organ disease. It may present with CNS involvement (somnolence, seizures, focal symptoms), cardiovascular syndromes (cardiac ischemia, severe hypertension), GI tract involvement (bowel necrosis, perforation, rectal prolapse, intussusception), hepatomegaly with increased LFT, as well as pancreatic disease (impaired glucose tolerance).

• Epidemic occurences of E. coli O157:H7 are related to water ingestion, raw beef consumption and bovine contact, dairies, sprouts/lettuce, as well as person-to-person contact.

Morning Report Questions

Describe the indications for renal replacement therapy.

1. Oliguria occurs in 60% of children, and anuria in 40%. Proteinuria and hemuria is common.
2. Peritoneal dialysis or hemodialysis should be considered when fluid and electrolyte imbalances cannot be corrected by replacement fluids or when fluid overload compromises cardiac or pulmonary function. A ureum > 35 mmol/L or GFR < 10 ml/min may be a similar indication.
3. Dialysis is indicated in 60% of pediatric STEC-HUS patients.

List the most important differential diagnoses to be considered in this context.

1. In the absence of microangiopathic hemolytic anemia and thrombocytopenia, severe enteric infection with hemorrhage-prone pathogens (SSCY: Shigella, Salmonella, Campylobacter, Yseria). Renal function impairment may be pre-renal due to volume depletion.
2. DIC may present with a similar laboratory pattern, and is indicated by prolonged coagulation tests (PT/aPTT).
3. Non-STEC HUS. Diarrea may be present too.
   1. complement-mediated HUS (positive family history, may be preceded by infectious prodrome),
   2. pneumococcal-associated HUS (pneumonia, meningitis).
4. Other thrombotic microangiopathic hemolytic anemias
   1. TTP (hereditary vs acquired, low ADAMTS13),
   2. drug-related TMA (typically acute onset), and coagulation-/metabolism related TMA (in infants, e.g. vitamin B12 metabolism defects).

All patient information have been changed, abstracted and anonymzed as to protect individual privacy.

Figures and additional information are available on Evernote (use the Gallery on the top right).

References

Tarr PI, Gordon CA, Chandler WL. Shiga-toxin-producing Escherichia coliand haemolytic uraemic syndrome. Lancet 2005.

Brodsky, R. A. (2015). Complement in hemolytic anemia. Blood, 126(22), 2459–2465. http://doi.org/10.1182/blood-2015-06-640995

Fitzpatrick, M. (1999). Haemolytic uraemic syndrome and E coli O157. BMJ (Clinical Research Ed.), 318(7185), 684–685.