Cryptic Shock – Identifying the Unseen (PART 1)

Case Presentation

A 68-year-old man presented to the Emergency Department with complaints of breathing difficulty and fever for three days. The patient is a known diabetic and hypertensive.

After detailed history taking, clinical examination, and radiological workup, the patient was diagnosed with right-sided lobar pneumonia (Community-acquired) and immediately started on intravenous antibiotics. In addition, necessary cultures and blood samples were taken for evaluation.

At the time of presentation, his vitals were HR – 92/min, BP – 130/70mmHg, RR – 30/min, SpO2 – 90% with RA à 96% with 2L O2. He underwent bladder catheterization.

During the 1st hour in the ER, the patient had a very low urine output, which continued for the next few hours. Lactate levels were more than 4mmol/L.

Based on the symptoms, oliguria, and hyperlactatemia, the patient was diagnosed to have sepsis and was initiated on fluid resuscitation. After 2 hours, the patient remained oliguric still, and his BP declined to 120/70mmHg.

After 6 hours, the patient’s BP became 110/60mmHg (MAP – 77). He became anuric and developed altered sensorium. Since he did not meet the criteria of septic shock, he was continued on IV fluids and antibiotics.

After 12 hours, the BP became 80/40mmHg (MAP – 63mmHg) à developed Multiorgan Dysfunction Syndrome. He was then started on vasopressors and mechanical ventilation.

By day 3, the patient further deteriorated and went into cardiac arrest. ROSC was not achieved.

Case Analysis

The treatment initiated was based on protocols like Surviving Sepsis Guidelines and Septic Shock management. So how did the process fail in order to adequately resuscitate this patient? Could something have been done more differently?

The case you read above is a very common scenario. Approximately 30% of the people coming to the ER are hypertensive, and around 10% have diabetes mellitus. They form a huge population, among whom the incidence of any other disease increases their morbidity and early mortality.

Before we delve into the pathology in these patients, let us look at the basic definitions of shock/hypotension.

  • SBP < 90mmHg
  • MAP < 65 mmHg
  • Decrease in SBP > 40mmHg
  • Organ Dysfunction
  • Hyperlactatemia
  • Shock: A state of circulatory insufficiency that creates an imbalance between tissue oxygen supply (delivery) and demand (consumption), resulting in end-organ dysfunction.
  • Septic Shock: Adult patients can be identified using the clinical criteria of hypotension requiring the use of vasopressors to maintain MAP of 65mmHg or greater and having a serum lactate level greater than 2 mmol/L persisting after adequate fluids resuscitation.
  • Cryptic Shock: Presence of hyperlactatemia (or systemic hypoperfusion) in a case of sepsis with normotension.

Based on all the information given above;

  1. what do you think was wrong with our patient?
  2. What kind of shock did he have?
  3. Could we have managed him any other way?
  4. When should we have started inotropes?
  5. Did the fact that he was hypertensive and diabetic have to do with his early deterioration? If so, how?
  6. When did the patient-first develop signs of shock?
  7. What are the different signs and symptoms of shock, and how are they recognized in the ER?

Keep your answers ready… 

Part 2 of Cryptic Shock Series – Vascular Pathology and What is considered ‘Shock’ in Hypertensive patients

Part 3 of Cryptic Shock Series – Individualised BP management

Part 4 of Cryptic Shock Series – Latest Trends

References and Further Reading

  1. Ranzani OT, Monteiro MB, Ferreira EM, Santos SR, Machado FR, Noritomi DT; Grupo de Cuidados Críticos Amil. Reclassifying the spectrum of septic patients using lactate: severe sepsis, cryptic shock, vasoplegic shock and dysoxic shock. Rev Bras Ter Intensiva. 2013 Oct-Dec;25(4):270-8. doi: 10.5935/0103-507X.20130047.
  2. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016 Feb 23;315(8):801-10. doi: 10.1001/jama.2016.0287.
  3. Shankar-Hari M, Phillips GS, Levy ML, Seymour CW, Liu VX, Deutschman CS, Angus DC, Rubenfeld GD, Singer M; Sepsis Definitions Task Force. Developing a New Definition and Assessing New Clinical Criteria for Septic Shock: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016 Feb 23;315(8):775-87. doi: 10.1001/jama.2016.0289.
  4. Education Resources – Sepsis Trust
  5. The Research of Predicting Septic Shock – International Emergency Medicine Education Project (
  6. Sepsis – International Emergency Medicine Education Project (
  7. Empiric Antibiotics for Sepsis in the ED Infographics – International Emergency Medicine Education Project (
  8. Sepsis – An Overview and Update – International Emergency Medicine Education Project (
Cite this article as: Gayatri Lekshmi Madhavan, India, "Cryptic Shock – Identifying the Unseen (PART 1)," in International Emergency Medicine Education Project, October 4, 2021,, date accessed: December 5, 2023

Question Of The Day #51

question of the day
Which of the following is the most likely cause for the patient’s condition?

This patient is in a shock state caused by left-sided pyelonephritis.

Shock is an emergency medical state characterized by cardiovascular or circulatory failure.  Shock prevents peripheral tissues from receiving adequate perfusion, resulting in organ dysfunction and failure.  Shock can be categorized as hypovolemic, distributive, obstructive, or cardiogenic.  The different categories of shock are defined by their underlying cause (i.e., sepsis, hemorrhage, pulmonary embolism, etc.) and their hemodynamics which sometimes overlap.  The diagnosis of shock is largely clinical and supported by the history, vital signs, and physical exam.  Additional studies, such as laboratory investigations, bedside ultrasound, and imaging tests help narrow down the type of shock, potential triggers, and guide management.  The chart below details the categories of shock, each category’s hemodynamics, potential causes, and treatments.   

The patient’s signs, symptoms, physical exam, and urine studies point towards an infectious etiology.  This patient is in septic shock, which is considered a type of distributive shock (Choice B).  Hypovolemic shock (Choice A), obstructive shock (Choice C), and cardiogenic shock (Choice D) are caused by other conditions reflected in the above table. 


Cite this article as: Joseph Ciano, USA, "Question Of The Day #51," in International Emergency Medicine Education Project, August 20, 2021,, date accessed: December 5, 2023

Sepsis – An Overview and Update

An Overview and Update

What is Sepsis?

Sepsis is a composite of symptoms and clinical signs that correspond to infection within a patient. This clinically heterogeneous syndrome may be fatal due to the extensive inflammatory processes and organ dysfunction it can provoke.

The New Definition of Sepsis

In 2016, after a revision by the European Society of Intensive Care Medicine and the Society of Critical Care Medicine, sepsis was redefined as “a life-threatening organ dysfunction caused by a dysregulated host response to infection.”

This new definition of sepsis means that the patient’s body, in response to infection, reacts by causing damage to its own organ structures, and this process can progress to the point where death can be an unfortunate end result.

Along with this up-to-date definition of sepsis, up-to-date criteria for evaluating sepsis were also provided; however, let’s first consider the causes of sepsis.

What is the Aetiology of Sepsis?

Sepsis can be caused by various organisms ranging from viruses to fungi to protozoans; however, bacterial infections are the main offenders. Vincent et al. (2009) concluded in the international EPIC II study that gram-negative bacteria were the principal perpetrators, accounting for 62%, while the gram-positives followed with a frequency of 47%. Of these groups, the principle organisms include:

  • Staphylococcus aureus and Pseudomonas at 20%
  • Escherichia coli at 16%

Different risk factors may predispose persons to become infected by these organisms.

Risk Factors

  • Non-Communicable diseases (Diabetes Mellitus, Chronic Kidney Disease)
  • Hemodialysis
  • Liver disease
  • Immunodeficient conditions
  • Trauma
  • The elderly, children, infants
  • Burns
  • Corticosteroid Use
  • Cancer
  • Prolonged Hospital Stay
  • Indwelling catheters

What is the Clinical Presentation of Sepsis?

The presentation of sepsis ranges from acute to insidious. There are cases where the patient may indicate a site of infection to cases where there is none apparent. Symptoms and signs of this syndrome generally include the following:

Another early sign of sepsis includes the presence of leukopenia or leukocytosis.
Along with these parameters, there are also specific signs within each organ system that must also be taken into account when investigating the source of primary infection or exploring the secondary effects of the same.

For example, when examining the respiratory system, listen for adventitious sounds or decreased breath sounds that may point to pneumonia and other chest infections. Respiratory causes of sepsis account for 42% of cases, according to the EPIC II study.

Patients who present with abdominal pain should be evaluated to rule out infection sources in abdominal structures such as the appendix, colon, pancreas, gallbladder. Other sources of infection may include the urinary tract and the prostate gland.

Patients with a history of trauma, wounds, and recent surgeries should be evaluated for any signs of wound infection (e.g., pain, erythema, purulent discharge, weeping wound, abscess formation)

In patients who are already admitted to the hospital and have been given invasive adjuncts, such as a central line, urinary catheters, and hemodialysis access sites, evaluate for inflammatory signs around the insertion site.

Warning Signs of Severe Sepsis

Sepsis progresses through a continuum that begins with a systemic inflammatory response syndrome (SIRS) and ends with multi-organ dysfunction syndrome (MODS), where mortality is almost inevitable. Its severest form is known as Septic Shock, a subcategory of sepsis where there is a great probability of mortality due to severe metabolic and circulatory irregularities.

The New Criteria for Evaluating Sepsis

The Sequential Organ Failure Assessment score, otherwise known as the SOFA score, is the new criteria used to evaluate sepsis. It replaces the SIRS Criteria.

SOFA takes into consideration six parameters that relate to specific organ systems. These systems are aligned with clinical signs and laboratory values, which fit into a numerical score ranging from 0 to 4, where 0 corresponds to normal values, and 4 corresponds to a high level of organ failure. See the image below, adapted from Vincent et al. (1996).

Since this criteria at its base enable physicians to assess the level of dysfunction occurring in the patient’s organ systems, the higher the score given, the more probable there will be an increase in mortality.

Using the SOFA criteria,  a score equal to and greater than 2 in the presence of confirmed or suspected infection corresponds to organ dysfunction. It indicates a mortality risk of around 10%.

The abbreviated version of the SOFA score, known as quick SOFA or qSOFA, is helpful for screening patients suspected to have sepsis by quickly evaluating three parameters, mental status, systolic blood pressure, and the respiratory rate.

REBELEM Blog (2016) qSOFA Score

Laboratory and Imaging

The general laboratory, imaging, and special studies for sepsis can include various tests depending on the suspected source of the infection, for example:

  • A Chest X-ray may show signs of pneumonia or any other lung infection.
  • CT imaging may reveal abdominal abscesses, perforation of the bowels.
  • An ultrasound can rule out pelvic sources of infection, as well as in organs such as the gall bladder.
  • Cardiac tests (electrocardiogram and troponins) may reveal suspected causes such as Myocardial Infarction.
  • Routine tests such as Complete Blood Count and Chemistry studies provide a baseline analysis for infection screening and organ dysfunction (kidney and liver).
  • Procalcitonin is a sepsis biomarker and increases in the presence of systemic bacterial infection.
  • Blood, urine, and source cultures should be taken for organism identification and antibiotic sensitivities.
  • Certain clinical presentations may necessitate abscess aspiration, lumbar puncture, or paracentesis.
  • Arterial blood gas is also a beneficial test for analyzing how septic a patient may be.

It is also important to note that serum lactate has become an important test in diagnosing sepsis, especially in relation to septic shock. (Lee and An, 2016)

The image below provides a summary of test results related to sepsis, as adapted from Mahapatra and Heffner (2020):

Treatment of Sepsis

The foundational aspects of treating sepsis rest upon rapid recognition and rapid remedy.

Schmidt and Mandel (2021) explain that resuscitation must be aggressively instituted in order to reperfuse the organs; just like antibiotic therapy, fluid resuscitation should be implemented within the first hour. It is given at 30 mL/kg and should be finalized by the third hour.

Initial antibiotic therapy should aim to cover both gram-positive and gram-negative organisms, any other considerations must be fully in line with the information found in the patient’s history, and physical examination. Where the source of infection necessitates surgical intervention, this must be pursued additionally.

The patient’s response to the treatments should be continuously monitored for improvements or worsening condition, and appropriate transfers should be pre-empted, for example, if the patient needs to be transferred to the Intensive Care Unit.

Key Points

  1. Sepsis is a clinically heterogeneous syndrome, which has a progression that can lead to severe cellular, metabolic, and overall hemodynamic dysfunction.
  2. If left un-recognized or, if it is not treated aggressively, the patient outcomes may be dim.
  3. The SOFA score is a criteria that is used in-depth and in a quick overview to assess the level of organ dysfunction in suspected or confirmed sepsis.
  4. Patients should be consistently monitored while exploring for the possible primary source.
  5. Sepsis is treated with rapid infusion of intravenous fluids and by using broad-spectrum antibiotics.
Cite this article as: Kohylah Piper, Antigua & Barbuda, "Sepsis – An Overview and Update," in International Emergency Medicine Education Project, June 28, 2021,, date accessed: December 5, 2023

References and Further Reading

Empiric Antibiotics for Sepsis in the ED Infographics

Empiric Antibiotics for Sepsis in the ED Infographics
Empiric Antibiotics for Sepsis in the ED Infographics
Cite this article as: Sarah Bridge, USA, "Empiric Antibiotics for Sepsis in the ED Infographics," in International Emergency Medicine Education Project, June 7, 2021,, date accessed: December 5, 2023

Recent Blog Posts By Sarah Bridge

The Research of Predicting Septic Shock

How computational medicine is changing critical care in 5 questions

Participating in Research

As a new school year approaches, many medical students are opting to take a gap year dedicated to research. This trend is unique for students not in MD/PhD programs in the USA who have a deep interest in understanding and participating in research. A popular emerging field for the future of health care and medicine, known as computational medicine, is become an integral part of patient care. Regardless of location, students, as well as interns and health care professionals around the globe who are interested in emergency and critical care medicine, should consider this unique area of study as a part of their research gap year.

In this blog entry for the International Emergency Medicine Education Project (iEM), I discuss the role of computational medicine in detecting sepsis, one of the most important diagnoses to detect early, with Professor Rai Winslow, Director of the Institute for Computational Medicine at The Johns Hopkins University. As outlined on the Institute’s website, computational medicine “aims to improve health care by developing computational models of disease, personalizing these models using data from patients, and applying these models to improve the diagnosis and treatment of disease.” Patient models are being used to predict and discover novel sensitive and specific risk biomarkers, predict disease progression, design optimal treatments, and discover novel drug targets. Applications include cardiovascular and neurological diseases, cancer, and critical care and emergency medicine (1).

Rai L Winslow, Director Institute for Computational Medicine, The Raj & Neera Singh Professor of Biomedical Engineering, The Johns Hopkins University

How is computational medicine changing critical care?

5 Questions

5 Answers

Why Sepsis

What was the starting point for your work on sepsis and septic shock in adults?

A starting point for my work on sepsis and septic shock was reading a paper that demonstrated how every hour of delayed treatment in patients with septic shock could lead to an eight percent increase in mortality, per hour. That statement really stood out because what it told me was the natural time course of evolution of the disease, and whatever was happening in septic shock, was happening very quickly. Because of this rapid disease progression, this suggested that accurate prediction of those patients with sepsis who would progress to septic shock must be based on data collected from the patient on a time scale of minutes rather than hours. The challenge was that this high-rate data is not routinely collected in hospitals.

Data and algorithms

What live data are the algorithms capturing from patients for studying and understanding sepsis and septic shock?

Today’s electronic health record (EHR) is typically used to store data such as vitals and lab results and clinical observations made at irregular intervals and at low rates. Given the rapid evolution of septic shock, we hypothesized that advanced prediction and early detection of septic shock must be based on data collected at the minute rather than hour time scales. This was the driving interest in developing a novel software platform called PhysioCloud. PhysioCloud captures physiological vital signs data at minute intervals from patient monitors. These data are then stored in a specialized database that is designed to capture large numbers of real-time data streams at high-rate. Data collection also includes waveforms, such as ECG, respiratory rates, and SpO2, sampled at 125 times per second. Nowhere else in the USA that I am aware of, is capturing these physiological data from patients, making them a part of the patient electronic health record. Our algorithm uses these high rate data, as well as low-rate data from the patient EHR, to predict those patients with sepsis who will develop septic shock.

The importance of the transition state to septic shock

Computational medicine and algorithms can be uncomfortable terms for medical students, interns and researchers who do not have experience with it. Simply put, how do research and studies such as this help doctors in emergency medicine and critical care units, in managing their patients?

Everyday critical care and emergency medicine physicians ask two questions of every patient they see: what is the state of my patient?; how will their state change over time? The latter is a prediction problem of the sort that data scientists often confront. In the context of sepsis, the physician would like to know if their patient will at some future time develop septic shock, or will their condition improve. If an algorithm can reliably predict those patients with sepsis who will develop septic shock at some future time point, then physicians will have a window of time in which they can intervene to prevent this transition from happening. Our goal was to develop such an algorithm. To do this, we utilized the obvious fact that if a patient has sepsis and their condition is getting worse and possibly evolving towards septic shock, it means their physiology must be changing over time as they get sicker. We, therefore, decided to develop a “risk score,” a number ranging between 0 and 1 that is the probability that a patient will develop septic shock. This risk score was computed in an optimal way from the minute by minute physiological vital signs data complemented by clinical data from the EHR. If this risk score exceeds a threshold value, then we decide that this patient with sepsis will develop septic shock at some future time point. This approach works very reliably, achieving high sensitivity and specificity. It’s the worlds simplest machine learning method. Predicting the transition from sepsis to septic shock can enable physicians the ability to follow their patients and see how various states are evolving over time, so that they can intervene to deliver earlier care. Right now, this approach is being applied in retrospective studies using patient data. In the future, we plan to compute this risk score in real-time, generating alerts for caregivers when the risk score exceeds threshold signaling that patients are likely to go into septic shock.


In a recent publication in Scientific Report (2), the new concept of a pre-shock state was outlined. How was this possible to do?

Our work hypothesized that it was possible to identify the presence of a physiological signature in sepsis patients before the clinical onset of septic shock was diagnosed. We were able to identify a signature to calculate a risk score for the pre-shock state. The changes in variables such as lactate and heart rate are so small; they are still statistically significant, but so small. When discussed with physicians, some say that they would not have noticed it. These variables are changing together in a small way, but the algorithm is able to catch the changes together and compute it into a risk score and make useful predictions. Some of our very new work not published yet shows that post-threshold, changes in patient risk score happen very quickly (30-60 minutes) and are very large. We have shown that the larger the post-threshold risk score, the more reliable is our prediction that the patient will go into shock. Positive predictive value can be as high as 80-90%.

Fluids and Vasopressors

Evidence-based studies and protocols such as the SOFA score (3), Surviving Sepsis Campaigns (4) are listed on the American College of Emergency Physician (ACEP) website (5) as well as the SALT-ED (6) and SMART (7) trials. These are referred to by emergency physicians in the emergency department, and EM residents are trained with these resources. How do these studies tie into computational medicine, machine learning and predictive analysis for developing septic shock?

Our algorithm looked at tens of thousands of patients, and computationally phenotyped them through every minute of data using the international consensus definition of septic shock, and based on early warning times, found clinical ground truth. We also discovered that the Sepsis 2 definition had a property that was temporarily unstable. This is to say that the state of a patient with sepsis as defined by Sepsis 2, was changing all the time, and it was not possible to predict ground truth. With found the Sepsis 3 definitions to be temporarily stable with few state transitions. The major factor was that the criteria in Sepsis 2 had included a diagnosis of SIRS before sepsis was considered as a diagnosis, and it was removed from 3. We believe that SIRS was causing frequent state changes, as an ambiguous diagnosis.

We are able to predict those patients with sepsis who will transition to shock many hours before they go into shock. We are also able to identify distinct temporal patterns of the risk score corresponding to patient populations with high (up to 60%) versus low (10-20%) mortality. For each of these groups, we looked at comorbidities, diagnoses such as kidney failure and cancer, but we do not know what the relationship is or what is different about these patient groups and the fact that they are in the 60% mortality pool. We know their physiology is saying they are in the mortality pool, but not why. What this means is how these patients are being treated could be the issue (physicians with different levels of training, and other factors involved in treatment decisions). In our work, patients were classified into high and low risk. We found that patients in the low risk received vasopressors and adequate fluid resuscitation and for patients in the high-risk pool, fewer had received vasopressors or fluids. The question is, why are these patients not getting these things. Our algorithm to predict the transition to septic shock can positively influence treatment decisions made by many physicians, to confirm the value of treatment and prevent the development of septic shock. We’ve also identified and know the time to look for proteomic and genomic biomarkers for the early predictive shock signature that could correlate with this high risk/these measures are not routinely done clinically, and this line of work could be very helpful in understanding the fundamental biology of the very rapid change in patient state when they cross the risk score threshold.

Thank you to Professor Winslow for taking the time to discuss the research involved in computational medicine and investigating the transition from sepsis to septic shock. In closing, regardless of medical specialty interests, medical students around the globe interested in taking a gap year to gain research skills will find the experience invaluable and will be introduced to new ways of thinking, writing, and understanding the scientific influences on patient management and health care. Research such as this in the USA can also be implemented at international hospitals and remote clinics, to further aid patient care and management. There are many areas of interest in which research is taking place in critical care units and emergency departments, and discovering the technology involved such as machine learning and computational medicine, is a step towards understanding the potential advances in the future of medicine and patient care.

Please feel free to share your own particular research area(s) of interest and pose any questions you may have in the comments section below.

References and Further Reading

  1. The Institute for Computational Medicine (ICM) –
  2. Liu R, Greenstein JL, Granite SJ, Fackler JC, Bembea MM, Sarma SV, Winslow RL. Data-driven discovery of a novel sepsis pre-shock state predicts impending septic shock in the ICU. Scientific reports. 2019 Apr 16;9(1):6145. –
  3. Faust J. No SIRS; quick SOFA instead. Annals of Emergency Medicine. 2016 May 1;67(5). –
  4. Surviving Sepsis Campaign (SSC) –
  5. ACEP Statement on SSC Hour-1 Bundle –
  6. Self WH, Semler MW, Wanderer JP, Wang L, Byrne DW, Collins SP, Slovis CM, Lindsell CJ, Ehrenfeld JM, Siew ED, Shaw AD. Balanced crystalloids versus saline in noncritically ill adults. New England Journal of Medicine. 2018 Mar 1;378(9):819-28. –
  7. Semler MW, Self WH, Wanderer JP, Ehrenfeld JM, Wang L, Byrne DW, Stollings JL, Kumar AB, Hughes CG, Hernandez A, Guillamondegui OD. Balanced crystalloids versus saline in critically ill adults. New England Journal of Medicine. 2018 Mar 1;378(9):829-39. –
Cite this article as: Bryn Dhir, USA, "The Research of Predicting Septic Shock," in International Emergency Medicine Education Project, August 12, 2019,, date accessed: December 5, 2023

From Experts to Our Students! – Sepsis