The deluge of data produced during medical care has typically been under-utilized or simply wasted. In the era of paper, this was explicable. However, in spite of nearly three decades of computerization, medical data remains difficult to access and organize, let alone use. Such a gap is both large and dramatic in the intensive care unit (ICU), where the complexity of illness and new possibilities unveiled by the unremitting march of technology transcend typical cognitive capabilities. In turn, this serves to further highlight the critical role of data support in evidence-based healthcare decision making.

From structured analysis to personalized treatment
Big Data’s case in the ICU, whose environment is both critical and intense by definition, is self-evident. One of the first arguments in its favour is that new ICU patients usually require extremely close monitoring. This is a highly data intensive process. The accumulation of data, in turn, can cause information overload in physicians who are providing the care.
Some experts foresee using Big Data in the ICU for structured analysis of complex decisions and the quantifying of expected benefits versus harms in different treatment options. Although such a tool has not been well received by several clinicians, it has considerable potential in terms of personalizing treatment. Today, ICU patients in particular can be provided with interventions that sustain life in spite of severe organ dysfunction. However, the treatments can also result in prolonged suffering with no guarantee of outcomes in line with patient preferences. Decision analysis based on Big Data might enable such concerns to be addressed.

Reducing uncertainty
There are several other practical drivers for Big Data in the ICU. Very often, ICU decisions have to be made with a high degree of uncertainty, and clinical staff may have minutes or seconds to make those decisions. These could cover issues such as knowing patient sub-populations that experience significant divergences in efficacy or unanticipated delayed adverse effects from drug treatments. At present, ICU practices vary due to either an absence of medical knowledge or conflicting opinions. Given time constraints, therapeutic decisions and choices depend largely on clinician preference and local practice patterns, leading to significant variability in quality of care.
Study shows scale of challenge in ICU interventions
As it stands, however, a large number of ICU interventions are not based on proven cases or standardized guidelines.
In 2008, a team at Erasmus Hospital in Brussels, Belgium, made a systematic review of 72 multi-centre randomized controlled trials evaluating the effect of ICU interventions on mortality and found that just 10 (about one in seven) showed benefit. 55 had no measurable value while as many as 7 (one in ten) were actually harmful.

Organizing critical care
Apologists for the lack of use of Big Data in the ICU point out that medicine can be as much art and science, and standardized protocols and best practices are not always sufficiently flexible. Such flexibility can indeed be imperative in an ICU, where decisions are subject to exceptional complexity and variability in patient status and clinical situation.

Nevertheless, a study on the concept of ‘organized care’ showed that applying W. Edwards Deming’s process management theory to manage variation in providing care can yield huge savings to the healthcare system. The study, titled ‘How Intermountain trimmed healthcare costs through robust quality improvement efforts’, was published in the June 2011 issue of ‘Health Affairs’. Its authors estimated that such efforts could save the US healthcare system about USD 3.5 billion (€3 billion) a year.
As a result, it may well be argued that variability in ICU practices is the result of a failure to research and establish evidence for a particular approach, in spite of the fact that both the data and the technology exist.

Scoring systems
Typical Big Data deployments in the ICU would be focused on the most expensive or high-risk parts of current clinical practice in critical care, and cover predictive alerts and analytics for complex case patients, decompensation and adverse events, intervention optimization for multiple organ involvement as well as triaging and readmissions.
Progress has already been made by using clinical data to infer high-level information in ICU scoring systems. These are largely used to compare ICU performance in terms of outcomes.
APACHE and SAPS
Two of the best known scoring systems are APACHE (Acute Physiology and Chronic Health Evaluation) and SAPS (Simplified Acute Physiology Score).
APACHE was designed to provide morbidity scores for a patient and help decide on a specific therapy. Methods to derive a predicted mortality from this score exist, but they are yet to be sufficiently well defined and precise.
SAPS was originally aimed at predicting mortality, originally for benchmarking. It has since been updated to provide a predicted mortality score for a particular patient or patient group by calibrating against recorded mortalities on an existing set of patients. SAPS can be used to compare the evolution in performance of an ICU over a period of time or compare treatment at different ICUs.

Variety of ICU databases in development
At present, ICU databases are being developed by hospitals/professional societies, academic institutions and medical equipment vendors. They structure and aggregate demographic data (age and sex of patient, condition or disease, co-morbidities, length of stay, date and time of discharge, mortality, readmission etc.) and provide such information on a hospital-specific basis. Rather than decision or standardization of protocols and practice, such databases simply provide monitoring and selective comparisons of ICU patient outcomes and costs – over time, or by region. However, there are new efforts to go further and build decision support tools.

Non-commercial databases
One good example of a non-commercial database is the Adult Patient Database (APD) from the Australia and New Zealand Intensive Care Society (ANZICS). It contains data from over 1.3 million patient episodes and is considered one of the largest single datasets on intensive care in the world. The database collects episodes from over 140 ICUs in Australia and New Zealand on a quarterly basis, and is used to benchmark performance of individual units.
The Danish Intensive Care Database (DID) is another non-commercial database, with data for over 350,000 ICU stays. DID made a big leap in introducing the ICU scoring indicator, SAPS II in 2010, which however remains less than 80% complete. DID quality indicators include readmission to the ICU within 48 hours and standardized mortality ratios for death within 30 days of admission using case-mix adjustment (age, sex, co-morbidity level and SAPS). Process indicators consist of out-of-hour discharge and transfer to other ICUs for capacity reasons.

Commercial databases
ICU databases are also being developed by medical technology vendors for commercial use. Cerner has created APACHE Outcomes, which has gathered physiologic and laboratory measurements from over 1 million patient records across 105 ICUs since 2010.  Although large, it still contains incomplete physiologic and laboratory measurements, and does not offer waveform data and provider notes.
Another commercial database known as eICU is provided by Philips. This telemedicine-intensive care support provider archives data from participating ICUs and is available to qualified researchers via the eICU Research Institute. The database size is estimated at over 1.5 million ICU stays, and it is reported to be adding 400,000 patient records per year from about 180 subscribing hospitals. As with APACHE Outcomes, eICU does not archive waveform data. However, provider notes are captured if entered into the software.

MIMIC
In contrast to commercial databases like eICU and APACHE Outcomes, MIMIC (Multiparameter Intelligent Monitoring in Intensive Care) is an open and public database with a host of clinical data from ICUs, vital signs, medications, laboratory measurements, observations and notes, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more.
Currently in its third generation, MIMIC provides a unique research resource with data from about 40,000 critical care patients. Hundreds of researchers from over 30 countries are given free access under data use agreements. In addition, several thousands of students, educators and investigators have used MIMIC’s waveform data, which is freely available to all.

History
MIMIC is the fruit of a collaboration since the early 2000s between Beth Israel Deaconess (a unit of Harvard Medical School), the Laboratory of Computational Physiology at the Massachusetts Institute of Technology (MIT), and Philips Healthcare, with support provided by the National Institute of Biomedical Imaging and Bioinformatics.
MIMIC was launched as a research project to establish a critical care alert and display (CCAD) system and assist decision support in the ICU, on the basis of a large temporal ICU patient  research  database. The system generated abnormal clinical values as clinician alerts via a user interface designed to allow efficient and ergonomic display of data. Within a short time after launch, it was producing over 50 alerts per patient ICU day.

Unique capability has promise for modelling
The MIMIC database is considered unique due to its capability to capture structured and extremely granular data. This includes per minute changes in physiologic signals, as well as time-stamped treatments with dosages, and permits modelling individual response to clinical intervention, which, in turn, allows for improved risk-benefit calculation and prediction of outcomes.
Some of these models might be optimal to develop effective early triage in terms of level of care and monitoring, as well as the allotment of scarce human and technical resources. In turn, such tools could assist emergency departments facing limitations in ICU resources.

Findings
Recent observational studies on the MIMIC ICU database have yielded several findings of interest. These cover areas such as long-term outcomes of minor elevations in troponin, heterogeneity in impact of red blood cell transfusion, the optimization of heparin dosing to minimize chance of under- or over-anticoagulation and the impact of selective serotonin reuptake inhibitors (SSRI) on mortality. Researchers are also studying areas of potentially great impact such as determining the proper duration for a trial of aggressive ICU care among high-risk patients.

International expansion
The MIMIC database is being used to design and develop decision support tools. Outcomes of concern are not limited to mortality or length of stay, but will instead be extended to include factors such as the probability of discharge to a nursing facility and expected duration of stay there, as well as the need for procedures such as hemodialysis or repeat hospitalization.
In spite of its clear utility, MIMIC is currently limited because its data is derived entirely from just one institution, namely Beth Israel Deaconess, and does not therefore account for practice variation across ICUs. There are however plans to expand the project to include data from ICUs in Britain and France.

Standard methods of decontamination, such as Disinfector Washers (e.g. using hot alkaline solutions and surfactants), are known to be inconsistently effective in removing protein from surgical instruments. Advanced Ultrasonics offers an exciting and tantalizing alternative. Preliminary results suggest that intense cleaning using “advanced ultrasonic technology” can potentially result in disinfection without the need for any thermal or chemical methods.

by David Jones

In the UK, concerns about Creutzfeldt-Jacob Disease (CJD) date back to the mid-1980s when an outbreak of Bovine Spongiform Encephalopathy (BSE, a similar transmissible neuro-degenerative brain disease) in cattle raised concerns that the disease might be transmissible to humans. Confirmation came in 1996 [1] that BSE can indeed lead to a form of human CJD (variant (v)CJD) that particularly affected younger adults. This resulted in widespread public health concern, heightened again a few years ago when a study in the British Medical Journal [2] suggested that as many as 1 in 2000 Britons may be infected with the abnormal prion protein that causes vCJD. To date there have been 178 deaths due to vCJD in the UK with a few more elsewhere [3]. In both model experiments and in actual human studies it has been shown that the prion protein is readily transmitted on stainless steel instruments from one animal to another.
vCJD highlighted to clinicians and decontamination / sterile services professionals alike, the critical requirement to remove protein, as well as other infectious agents, from neuro-surgical and other reusable surgical instruments. In addition to the risk of patient-to-patient transferal of vCJD prions, there is a danger that bacteria hidden in or under any residual protein e.g. biofilms could also be passed on. A recent study in the journal Acta Neuropathologica [4] also highlighted the potential dangers associated with cross-contamination of neurosurgical instruments with the peptide amyloid beta (Aβ), a substance implicated in brain hemorrhages and Alzheimer’s disease.  
Standard methods such as Disinfector Washers (e.g. hot alkaline solutions and surfactants) are known to be inconsistently effective in removing protein from surgical instruments [5,6] and other difficulties in ensuring consistent cleanliness has led to a move towards single use instruments. However, questions remain as to how manufacturers of single use instruments can achieve consistent cleanliness and sterility when modern, well equipped Sterile Service and Decontamination (SSD) units apparently cannot. Unfortunately, single use instruments are not always clean and sterile as recent unpublished investigations have shown.
In the UK, concerns about contamination mean that GPs and dentists, who have historically performed minor interventions such as lancing of boils, removal of small cysts and abscesses etc., are now being discouraged from doing so. This, in turn, is funnelling more patients to A&E departments, which are already under tremendous strain. Post-operative infections also add to strain on the health service, leading to extended hospital stays and bed-blocking.
There is a clear need for a new approach to improve the cleaning of surgical devices. “Commercial grade” ultrasonic cleaning systems have been available for a number of years and have been used as a first stage in the cleaning process.
Ultrasonics works via the process of cavitation. Transducers bonded to the base or side of a tank are excited by high frequency electricity causing them to expand and contract at very high speed. This mechanical action causes high speed downward flexure of the radiating tank face. The speed of this movement is too fast for the water in the tank to follow, resulting in the production of vacuum chambers. On the upward flexure the vacuums are released in the form of vacuum bubbles which rise up through the fluid until they hit an object, upon which the bubbles implode under high pressure, thus drawing away any contamination that may be on the surface of the object. 
However, it has been shown that machines used in sterile services departments in the past have an erratic distribution of sound that does not consistently render instruments clear of residual protein. It was felt by many that a new way of applying ultrasound into a fluid was required. To achieve the safe cleaning of these items, the sound needs to be applied in a way that is both even as well as intense, with no gaps in activity where cleaning would not be effective.
 
In order to develop a new cleaning technology, a reliable method for measuring residual protein was needed and agreement reached on acceptable levels. The UK HTM 01-01 Guidance on the Management and Decontamination of Surgical Instruments [7], released in 2016, specifies that “there should be <5µg of protein in situ, on a side of any instrument tested”. In situ testing is specified since: “detection of proteins on the surface of an instrument gives a more appropriate indication of cleaning efficacy related to prion risk” than the swabbing techniques used in the past [8,9,10]. Currently the ProReveal system, from Synoptics Health, Cambridge UK, is the only in situ system on the market worldwide. As well as high levels of accuracy, the system also identifies the precise location of any remaining proteins on the instrument. To comply with UK HTM 01-01 guidance, therefore, any new cleaning system, ultrasonic or otherwise, needs to be validated against the levels of detection offered by ProReveal.
A second issue to be addressed by any ultrasonic cleaning technology is how to measure the ultrasonic activity. HTM 01-01 states that machines should be periodically tested for ultrasonic activity.
Historically, the only method available to Sterile Services Managers and AED’s for validating the activity in an ultrasonic tank has been to insert a piece of aluminium foil into the fluid for a set time and then visually analyse the indentations in the foil to determine the ultrasonic activity. This is a somewhat inaccurate way of validating what is a critical phase in the decontamination process. Troughs of sound can be either macroscopic or microscopic and, as such, the reliance on sight alone is unacceptable when such high levels of consistent cleanliness are expected.
With both these issues in mind, Alphasonics (a Liverpool/UK company with over 25 years’ experience in the field of ultrasonic cleaning systems) launched the ‘Medstar’ project with a view to developing ‘advanced ultrasonic technology’ for cleaning surgical equipment.  The project started in 2013 but it was not until 2015 when a ProReveal was purchased that substantive advances were made.  Progress then accelerated quickly and over a 3-year period, a point was reached whereby instruments could be rendered “completely” free of residual protein, as assessed by ProReveal technology.

To overcome the problems around accurately measuring ultrasonic activity, the world’s first Cavitation Validation Device (CVD) was developed from 2016 to 2018 which, for the first time, allows the validation of ultrasonic cleaning devices by listening exclusively for cavitation noise.
CVDs are included within most Medstar systems and the below graphs show how Medstar devices perform compared to existing ‘commercial grade’ ultrasonic cleaners (Data on File).
It is this unique, intense ultrasound technology that is so effective in removing protein residue from medical devices, as measured by the in situ ProReveal method.  To assess the effect on removal of bacteria, a UKAS (UK Accreditation Service) accredited laboratory was engaged to carry out independent trials. Instruments were contaminated by the laboratory, first with Enterococcus faecium and Staphlyococcus aureus (as specified within ISO15883 annex N- “test soils and methods for demonstrating cleaning efficacy”) and then with “dirty” conditions (specified in ISO13727). They were then cleaned in a Medstar device. Since all residual protein was being removed, the question arose: was the (now exposed) bacteria also being removed by the intense ultrasound?
Work is on-going, but preliminary results suggest that intense cleaning using ‘advanced ultrasonic technology’ can potentially result in disinfection without the need for any thermal or chemical methods.
Medstar devices have several other features to allow compliance with UK HTM01-01 guidance, such as the Generator Output Monitoring System- which constantly monitors the generator output and adjusts the input accordingly, thus ensuring that the system is always performing optimally. The CVD device is then used for periodic independent validation.
Advanced Ultrasonics offers an exciting and tantalizing alternative to thermal disinfection devices. The HTM01-01 UK guidelines are only the start of things to come and it is already widely recognized that the 5µg limit set out in the guideline is still too high. The many trials undertaken by the manufacturer have clearly shown that the Medstar range of equipment leaves no more than 0.5µg of residual protein per side on an instrument and as such renders the bacteria fully exposed to the intense, very even, action of the ultrasound and enzymatic chemicals.
High throughput systems are also available that would be of great benefit to single-use instrument manufacturers and SSD units alike. These systems will deliver a consistently lower residual protein count and a better log reduction than thermal disinfection devices.

References
1. John Collinge, Katie CL Sidle, Julie Meads, James Ironside, Andrew F Hill. Molecular analysis of prion strain variation and the aetiology of “new variant” CJD. Nature, 1996; 383(6602), 685. doi:10.1038/383685a0
2. Gill O, Spencer Y, Richard-Loendt A, Kelly C, Dabaghian R, Boyes L, Linehan J, et al. Prevalent abnormal prion protein in human appendixes after bovine spongiform encephalopathy epizootic: large scale survey. British Medical Journal, 2013; 347, 11.
3.   See www.cjd.ed.ac.uk/sites/default/files/figs.pdf
4. Jaunmuktane Z, Quaegebeur A, Taipa R, Viana-Baptista M, Barbosa R, Koriath C, Sciot R, et al. Evidence of amyloid-β cerebral amyloid angiopathy transmission through neurosurgery. Acta Neuropathologica, 2018; 135(5), 671–679. doi:10.1007/s00401-018-1822-2
5. Murdoch H, Taylor D, Dickinson J, Walker JT, Perrett D, Raven NDH, Sutton JM. 
Surface de-contamination of surgical instruments – an ongoing dilemma. Journal of Hospital Infection 2016; 63: 432-438
6. Baxter RL, Baxter HC, Campbell GA, Grant K, Jones A, Richardson P, Whittaker G. Quantitative analysis of residual protein contamination on reprocessed surgical instruments. J Hosp Infect 2006; 63, 439-444.
7. Department of Health and Social Care. Health Technical Memorandum (HTM) 2006; 01-01: management and decontamination of surgical instruments (medical devices) used in acute care.. Available: https://www.gov.uk/government/publications/management-and-decontamination-of-surgical-instruments-used-in-acute-care. Last accessed July 2018.
8. Nayuni N, Cloutman-Green E, Hollis M, Hartley J, Martin S, Perrett D. A critical evaluation of ninhydrin as a protein detection method for monitoring surgical instrument decontamination in hospitals. J Hospital Infection 2013; 84 97-102
9. Nayuni N, Perrett D.  A comparative study of methods for detecting residual protein on surgical instruments. Medical Device Decontamination (incorporating the IDSc Journal) 2013; 18 16-20
10. Perrett D, Nayuni N. Efficacy of current and novel cleaning technologies (ProReveal) for assessing protein contamination on surgical instruments 2014; Chapter 22 in Decontamination in Hospitals and Healthcare Edited by Dr. J.T. Walker, Woodhead Publishers, Cambridge, UK.

The author
David Jones
Alphasonics, Liverpool, UK
www.alphasonics.co.uk