Doctors working in the eight-bed Pediatric Intensive Care Unit at the Ramón y Cajal University Hospital in Madrid use point-of-care ultrasound extensively to evaluate the condition of critically ill children, and find it essential to their work. Dr José Luis Vázquez Martínez, Head of UCIP at Hospital Ramón y Cajal, with over 25 years’ experience in pediatric intensive care medicine, explained.

Point-of-care ultrasound (POCUS) is used extensively in our unit, allowing comprehensive, head-to-toe assessment of critically ill children, including respiratory, oncology and post-operative cardiac patients, as well as those being treated for sepsis or multiple trauma. The POCUS approach allows not only an initial diagnosis, but also routine monitoring of treatment to see whether or not a patient’s condition changes, enabling alternative strategies to be implemented if there is no improvement.

POCUS helps pediatric doctors in many ways. For example, ultrasound scans enable evaluation of a patient’s hemodynamic state, looking at their heart function and blood volume to see if these factors are contributing to respiratory failure. Conversely, doctors can see if a lung problem, such as pneumonia, is affecting the heart. For a patient in a coma due to multiple trauma, ultrasound is used to look for signs of bleeding – a potential cause of unexplained anemia – and to assess the intracranial pressure. It is also used to monitor kidney function in children with blood pressure problems, and visualize intestinal indications of sepsis. In addition, ultrasound guidance can be used for endotracheal intubation. In short, broader applications that we did not anticipate until very recently.

We have used ultrasound in our PICU for more than a decade, and have always had SonoSite systems, upgrading them as new technology is introduced. In the beginning, when my knowledge was more limited, the aim was to perform clinical echocardiography but, when the SonoSite representative showed me the linear probe and the various techniques available, it was as if I was being shown electricity after using candles! It was amazing, a real turning point in the use of ultrasound, and everyone recognized it as a step forward in the pediatric intensive care world. For the patients, a major benefit of ultrasound is that exposure to radiation can be reduced. Before ultrasound, X-ray examinations were performed two or three times in the first few days after admission to try to establish the cause of the problem, often with limited success. With ultrasound, we can scan the patient as often as necessary, implementing treatment and monitoring its effect without exposing the child to more radiation.

In PICU, we consider an ultrasound system essential – there is nothing else that gives us so much information, so quickly and non-invasively – and today we have a dedicated Edge II ultrasound system with linear, including hockey stick, and adult and pediatric cardiac transducers. It is in constant demand and is a perfect fit for our work, fulfilling all our expectations. All my colleagues use it, and we are very satisfied with it. The system is high quality and ergonomic, and strikes a good balance between image quality and ease of use. It is also quick to boot up, which is crucial for an instrument that is frequently moved between different beds in the unit. Robustness is vital too; if a patient deteriorates, we may have to move any equipment surrounding the bed very quickly to create space to treat them. However careful you are, there is always the risk of unintentional knocks to the system.

A while ago someone said to me that they ‘sell ultrasound machines but don’t offer training’, but this view isn’t enough – it’s very short-sighted – training is very important. Ramón y Cajal pioneered the use of ultrasound in PICUs across Spain, and was the first hospital to offer external training courses for doctors from other facilities, initially focused on clinical echocardiography. Over time, this has expanded to include neuromonitoring, respiratory and abdominal monitoring. I acquired my ultrasound experience through a combination of external training in adult ultrasound and practical, hands-on learning, and am largely self-taught. If courses like these had been available when I started using ultrasound, I would have saved so much time.
FUJIFILM SonoSite is clearly committed to organising and supporting ultrasound training, and this is unquestionably a great benefit to the scientific community – long may it last!      

Today, we are seeing a boom in the use of ultrasound in pediatric care, as it non-invasively provides immediate information in situations where time is of the essence. Our advice to people attending our training courses who do not have – or have to share – an ultrasound system is to tell their hospital managers that, just like a ventilator, it is an essential piece of equipment for an intensive care unit.

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Neonatal intensive care units can be noisy places which can disturb the sleep patterns of the youngest patients in the hospitals and have a negative effect on their health. In an effort to ameliorate this, some NICUs have set quiet times to limit exposure to noise. However, little was known about the effects of the ‘quiet time’ on infant health and it is only now according to a recent study in The Journal of the Acoustical Society of America that researchers have demonstrated its beneficial effect. The study, one of the first in this field, examined the effects of quiet time implementation in multiple NICUs on infants up to 18 months after implementation. They analysed how each NICU’s soundscape changed throughout the day and how this affected infant heart rates. They found that certain stressful pitches were actually quieter in respect to their effect on infant heart rates and that very loud sounds occurred less frequently with the result that quiet time throughout the day was longer. The results provide a sense of which features of quiet time policies have the largest impact on infants in NICUs and they recommend using quiet time protocols to help NICU patients in addition to implementing architectural noise reduction strategies in NICUs.
In a separate, but related study published in Sleep last year, researchers showed that preterm newborns sleep better in NICUs while hearing their mother’s voice. The study explored the possibility that infants’ exposure to their mother’s voice in the NICU could modulate the impact of noise in the NICU. The results indicate that newborns in a NICU were less likely to be awakened by noises when a recording of their mother’s voice was playing. The study also found that newborns born at or after 35 weeks’ gestation show sleep-wake patterns that appear to respond increasingly with age to recorded maternal voice exposure. Similar associations were not found for infants born before 35 weeks’ gestation. It appears that exposure to a mother’s voice recording may insulate NICU patients from some of the impact of unavoidable noise by reducing the likelihood of wakefulness during the highest peak noise levels. Because of this, the researchers suggest that for infants who are ill or born prematurely and may require extended care in a NICU during a time of critical brain development, interventions designed to improve sleep may need to be tailored according to gestational age. As such, the impact of playing a recording of a mother’s voice, reading a story for example, may have a more significant impact for newborns who are near term gestation than for more premature infants.

Elderly women account for a large part of the world’s population. The number of females aged 60 and over is on course to cross one billion in 2050. This would correspond to a tripling of the level from 335 million in 2000. Older women out-number older men, and this imbalance rises with age. Indeed,  the fastest growing sub-group among ageing women consists of those over 80. Globally, there are about 125 women for every 100 men in the over-60 age group. Among the over-80s, the gap is much higher, at 190 women for 100 men.

Longer but not necessarily healthier lives
The increase in number of elderly women has been accompanied by the growth of their very specific health needs. Although women in Europe outlive men by six years, the difference in healthy life expectancy is only nine months. In effect, their extra years are severely burdened by disease and ill health.
In spite of such facts, there is a remarkable lack of data specifically focused on the health of elderly women. For instance, figures from the European statistical service, Eurostat, show standardized death rates per 100,000 inhabitants for all women, and for women under-65. Although it would be possible to determine the figure for women greater than 65 years in age, it is remarkable that this is not provided on the Eurostat site.

Data limitations
In 2005, a group called Older Women Network Europe (OWN-Europe) observed that though there was an abundance of studies on ageing, there was little gender analysis of potentially major differences in health on ageing women versus ageing men.
Ironically enough, OWN-Europe’s own website (www.own-europe.org) has been taken over by an entity dedicated to promoting anti-cellulitis stockings in the Japanese language. The organisation itself has been subsumed into AGE Platform Europe, which is a forum promoting awareness about issues affecting the aged in general, rather than differences in issues and concerns between elderly women and elderly men. As noted, this was OWN-Europe’s critique to begin with.
Another organisation, Dublin-based European Institute of Women’s Health (EIWH) has since sought to fill this gap. Though also concerned with general women’s health issues, it has an elderly-focused approach on key topics of interest – for example, providing data-based position papers on specific risks to elderly women, as compared both to men and younger women, in areas such as dementia, breast cancer, cardiovascular disease etc.

Age-related risks for women
Differences in Eurostat cause-of-death rates for women under 65 years in age versus all women yield some interesting conclusions.
Diseases of the cardiovascular system (circulatory disease and heart disease) account for the largest share of deaths in elderly women in Europe, well ahead of cancer. Lung cancer results in about
65 percent higher deaths than breast cancer, with colorectal cancer only slightly behind.
There is a steep rise in the age-related risk of dying from cardiovascular disease (CVD). This is outweighed slightly by the much smaller rate of death from respiratory disease. The age-related risk increase is also marked in dying from diseases of the nervous system.  Once again, the risk of older women dying from lung cancer as compared to younger women is significantly higher than breast cancer, while the age-related growth in risk is also high for colorectal cancer.

Lack of attention: The CVD example
Attention to specific age-related health issues in women has been inadequate.
For example, though it has been long known that CVD is a significant cause of female death, women present different symptoms than men. For example, a heart attack in a woman is often confused with indigestion—not pain in the chest. Women are also less likely to seek or to be provided with medical help and to be properly diagnosed until late in the disease process. Such factors are believed to explain why women are less likely to survive a heart attack, particularly when treated by a male doctor.

Other scourges
On the other side of the spectrum are conditions such as osteoporosis and osteoarthritis, which do not result in death, but lead to chronic pain and limit quality of life. They do not get adequate attention, since they are seen as an inevitable part of ageing – or as less serious conditions than heart disease or cancer. Both osteoporosis and osteoarthritis have a high propensity for women.

Osteoporosis: early start for women
Osteoporosis, for example, is four times more common in women aged over 50 than in men. One of the reasons is that women have a lower peak bone mass and show a younger onset of bone loss compared with men – on average, by 10 years.
For women, rapid declines in bone mass occur in the 65-69 age group as opposed to 74-79 for men. A second factor playing a role here are the hormonal changes which occur at menopause; these can alter calcium composition in a woman’s body.
Meanwhile, initiatives like hormone replacement therapy (HRT), once widely used in the wealthier countries, have become mired in controversy. Recent studies suggest that rather than prevent heart disease after menopause as was originally believed, HRT is associated with an increased risk of stroke and heart disease among some ageing women.

Osteoarthritis in one of 5 elderly women, twice rate in men
Osteoarthritis too shows the above patterns. This degenerative joint disease is associated with ageing and principally affects the articular cartilage. It impacts on joints which have been stressed over the years – such as the fingers, the knees, hips, and the lower spine region. 80% of osteoarthritis patients have limitations in movement, and 25% cannot perform their major daily activities of life.
Globally, an estimated 18 percent of women aged over 60 years have symptomatic osteoarthritis, which is almost twice a rate of 9.6 percent reported in men. Moreover, the incidence of osteoarthritis in the 60-90 age group rises 20-fold in women as compared to 10-fold in men.

Osteoarthritis and CVD
Osteoarthritis, in particular, has serious implications for another major problem, namely CVD. Meanwhile, some studies have demonstrated a high prevalence of CVD in osteoarthritis patients. One found that 54% of people with knee and hip osteoarthritis had co-existing CVD.

Need for more research on women
The above observations underwrite a need for research on diseases and health conditions of concern to women in general, and elderly women in particular.
Although CVD is one of the best known examples of differences between the sexes in symptomatic and other responses to disease, there are other cases. For instance, among men and women smoking the same number of cigarettes, women are 20 to 70 percent more likely to develop lung cancer.
One of the first areas of attention is to increase the number of clinical trials dedicated to such issues and encourage the participation of women in trials.

After thalidomide, women discouraged in clinical trials
Low female representation in clinical trials became a structural problem after the US Food and Drug Administration (FDA) issued a guideline in 1977 banning most women of ‘childbearing potential’ from participating in clinical research studies. This was the result of drugs like thalidomide, which caused severe birth defects.
Nevertheless, few denied, even then, that new drugs were metabolized differently by men and women due to factors such as body size, fat distribution and the hormonal environment.
It soon also became apparent that even new life-saving drugs might not work as well in women as they did in men. Worse still was one study in 2001, which reported that female patients have a 1.5 to 1.7-fold greater risk of developing adverse drug reactions than men, due to gender-related differences in pharmacokinetics as well as immunological and hormonal factors.
In the three years 1997-2000, eight of the 10 drugs for which the FDA withdrew approval had harmful side effects for women.

US changes approach, but gap still large
In the late 1980s, the FDA issued new guidelines to encourage inclusion of more women in studies and in 1993, formally rescinded its policy discouraging women from participating in studies.
Additional studies between 2011 and 2013 evaluated the inclusion and analysis of women in federally-funded randomized clinical trials. The researchers found that most such US studies, which were not sex-specific, had an average enrolment of 37% women. However, almost two out of three studies did not specify their results by sex and did not explain why the influence of sex in their findings was ignored.

The European case
The situation is similar in Europe. For instance, in spite of the role of CVD in female mortality, a EuroHeart report found that women comprised only a third of CVD trial participants, while one of two studies did not report the results by gender. Until the 1990s, clinical research in Europe followed the US lead and focused mainly on men. As the US began to shift stance towards encouraging women in trials, Europe followed suit, using the Inter-national Conference on Harmonisation (ICH) as a vehicle. ICH guidelines require Phase I response data be obtained for relevant sub-populations “according to gender.” However, many of the require-ments offer opt-outs with wording like “if the size of the study permits,” or recommend that demographic subgroups be “examined.”

New Regulation on Clinical Trials
EU rules on clinical trials are due to be overhauled after a new Clinical Trial Regulation (Regulation (EU) No 536/2014) comes into application. The Regulation harmonises clinical trial assessment and supervision via a Clinical Trials Information System (CTIS), which will be maintained by the European Medicines Agency (EMA).
The Regulation was adopted in 2014, but will enter into force after the CTIS is certified through an independent audit. This is still ongoing.
The new Regulation recommends that “gender and age groups” which would use a medicinal product should participate in its clinical trials. However, it still leaves an opt-out if exclusion is “otherwise justified in the protocol”, although “non-inclusion has to be justified”.
In other words, the jury is still out.

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.