which is why safety is better subsumed under reliability. There are six elements for improv ing reliability: \200 \200 \200 \200 \200 \200 Standardization Simpli \201 cation Redundancy Teamworking and communication Partnership Ed ucation Application of these principles has been shown to reduce bacteraemias associated with centr al venous catheters effectively to zero. Standardization: medical staff are better at creat ing guidelines and protocols than implementing them in their own practice. Individual varia tions in clinical practice increase the opportunity for error. The Surviving Sepsis Campaig n provides an example of evaluating evidence and formulating clinical guidance. Simpli \201 cation: reducing complexity minimizes opportunity for error by removing additional or unfamiliar steps in the processes of care, e.g. by reducing the number of hando- vers ( \200\230hand-offs \200\231) and using common forms of information transfer. Sepsis Care Bun dles simplify by aggregation. Electronic prescribing systems with forcing functions limit c hoices to correct actions. Redundancy: means more than one way of achieving the correct goa l. This includes reminders, prompts, multiple communication channels and empowering team me mbers to draw attention to errors, and adequate staffing to reduce fatigue. One approach is to ask the question \200\230what are the main safety risks for this patient today? \200 \231 Effective teamworking and communication: encourages group participation, mutual perfor mance monitoring,and explicit daily goals (e.g. on the ICU chart), as well as making time f or listening to others. Partnership: means shared goals. For example, involving close relat ives in the bedside ward rounds can enhance shared purpose, enable families to feel that th ey are Management and prevention When error results in harm, we have two professional responsibili ties. One is to provide appropriate care for the victim(s) \200\224the patient, the family and the staff involved. The second is to learn from the episode and to take steps to minimi ze the risk of repetition. This requires transpar- ency, honesty and a supportive environme nt. Structured methods of investigation make it easier to learn from defects in healthcare and impose a necessary discipline on the less effective morbidity and mortality meeting. Pr evention is the essence of safety improvement efforts. However, too narrow focus on safety may have the unintended consequence of impeding ef \201 cacy of care, organizational & CORPORATE CULTURE CONTRIBUTORY FACTORS INFLUENCING PRACTICE CARE DELIVERY PROBLEMS defenses/ BARRIERS Work/environment factors Unsafe acts Team factors Management decisions and organizational processes Errors Individual (staff) factors Incident Task factors Violations Patient factors LATENT FAILURES ERROR & VIOLATION PRODUCING CONDITIONS ACTIVE FAILURES Fig. 33.9.1 A systems approach to analyzing accidents (from Woloshynowych et al., 2005). 566 J BION contributing to care and improve the accuracy of informa- tion \202 ows. Education: in th e clinical environment, safety, reliability and teamworking can be included in simulation-b ased learning. Learning from error must be embedded in undergraduate and postgraduate natio nal training programs (see www. cobatrice.org) and integrated across disciplines and over p rofessional lifetimes. Kohn LT, Corrigan JM, Donaldson MS, eds. To err is human: building a safer health system. W ashington, DC: National Academic Press, 2000. NICE guideline. Acutely ill patients in hospital: recognition of, and response to, acute il lness in hospitalized adults. National Institute for Clinical Excellence 2007. http://www.n ice.org.uk/ CG50 Patient Safety Observatory. Safer care for the acutely ill patient: learning from serious i ncidents. National Patient Safety Agency 2007. http://www.npsa.nhs.uk/health/resources/pso Reason J. Latent errors and system disasters human error. Further reading Bion JF, Heffner J. Improving hospital safety for acutely ill patients. A L ancet quintet. I: Current challenges in the care of the acutely ill patient. Lancet 2004; 3 63: 970 \200\2237. See also the subsequent four articles in this \201 ve-part series. Cambridge: Cambridge University Press, 1990. Rothschild JM, Landrigan CP, Cronin JW, et al. The Critical Care Safety Study: the incidenc e and nature of adverse events and serious medical errors in intensive care. Crit Care Med 2005; 33: 1694 \200\223700. Blendon RJ, DesRoches CM, Brodie M, et al. Views of practicing physicians and the public on medical errors. N Engl J Med 2002; 347: 1933 \200\22340. Weingart SN, Wilson RMcL, Gibberd RW, et al. Epidemiology of medical error. BMJ 2000; 320: 774 \200\2237. Brennan TA, Leape LL, Laird NM, et al. Incidence of adverse events and negligence in hospit alized patients. N Engl J Med 1991; 324: 370 \200\2236. Woloshynowych M, Rogers S, Taylor-Adams S, et al. The Investigation and analysis of critica l incidents and adverse events in healthcare: a review of the literature. Health Technol As sess 2005; 9: 1 \200\223143. http://www.who.int/patientsafety/en/ This page intentionally left blank 568 R MORENO & S AFONSO DE CARVALHO Severity of illness scoring systems The evaluation of severity of illness in the critically ill patient is made through the use of general severity scores and general outcome prognostic models. 1 Severity scores are in struments that aim at stratifying patients based on their severity, assigning to each patie nt an increasing number of points (or score) as their sever- ity of illness increases. Organ failure is not a static process and the degree of dysfunction varies with time during the course of disease. The variables chosen to evaluate each organ need to be objective, s imple and available but reliable, routinely measured in every institution, speci \201 c to the organ in question, and independent of patient variables, so that the score can be easi ly calculated on any patient in any ICU. 2 Prognostic models usually use a given set of prognostic variables and a certain modeling equation. aim to stratify patients according to their seve rity of illness based in their respective risk aim at predicting a certain outcome \200\224 usually the vital status at hospital discharge. There is no general agreement about how to assess organ dysfunction effectively, but all th e widely used systems include six key organ systems (cardiovascular, respiratory, hematolog ical, central nervous, renal and hepatic), evaluated through a combination of physiological (e.g. PaO2) and therapeutic (e.g. use of vasopressor agents) variables. Commonly used orga n dysfunction/failure scores are the Multiple Organ Dysfunction Score (MODS). the Sequentia l Organ Failure Assessment (SOFA) score and The Logistic Organ Dysfunction System (LOD) sco re. Usually these scores, describing a patient \200\231s progress, are registered at admiss ion and then once a day until ICU discharge. Several derived measures are often computed su ch as the maximum score or the delta (maximum minus admission) score. In Europe, the SOFA s core is popular. It evaluates 6 organs/ systems, with each scoring between 0 and 4. Zero is normal, 1 \200\2232 dysfunction and 3 \200\2234 failure. Individual organ function can thu s be assessed and monitored over time, and an overall global score can also be calculated ( see Table 33.10.1. Other outcomes, in both the short- and the long-term, can eventually be considered, but mos t of them are more prone to bias and manipulation or are of little interest for the patient s, their families and the healthcare providers. Available instruments Several hundred severity scores have been proposed thus far. Most of them have been developed to be used in spe- ci \201 c populations, such as children or inf ants, or patients with certain diseases (peritonitis, AMI). They can be divided in two broa d categories: Multiple organ dysfunction/failure scores MOD scores have been designed to qu antify organ dys- function and organ failure in adult patients in ICUs. All these instrumen ts have been developed with some similar principles in mind: \200 Organ failure is not a simple all-or-nothing phenomenon, it is a spectrum or continuum of o rgan dysfunction from very mild altered function to total organ failure. Table 33.10.1 Sepsis-related Organ Failure Assessment (SOFA) score 0 1 2 3 4 Respiratory PaO2/FiO2, mm3 Hamatological Platelets ( \227 1000/mm3) Liver Serum bilirubin, mg/dl ( mol/l) Cardiovascular Blood pressure >400 \211 400 \211 300* \211 200* \211 100* >150 \211 150 \211 100 \211 50 \211 20 <1.2 (<20) 1.2 \200\2231.9 (20 \200\22332) 2.0 \200\2235.9 (33 \200\223101) 6.0 \200\22311.9 (102 \200\223204) >12.0 (>204) Dopamine > 15 or epinephrine > 0.1 or norepinephrine >0.1 Dopamine >5 or epinephrine \211 0.1 or norepinephrine \211 0.1 MAP \211 70 MAP <70 Dopamine \211 5 or dobutamine (any doses) Neurological Glasgow Coma Scale Renal Serum creatinine, mg/dl ( mol/l) Urinary output (l/d ay) 15 0 <1.2 (<110) 13 \200\22314 10 \200\22312 6 \200\2239 <6 1.2 \200\2231.9 (110 \200\223170) 2.0 \200\2233.4 171 \200\223299) 3.5 \200\2234.9 (300 \200\223440) <0.5 >5.0 (>440) <0.2 Adrenergic agents should have been administered for at least 1h in continuous IV infusion. Doses are in mcg/kg/min. With ventilatory support MAP = mean arterial pressure, mm Hg. 569 CHAPTER 33.10 Severity of illness scoring systems Severity of illness scoring systems The most popular are described below. Data collection should be as similar to the original model as possible. (This is speci \201 cally important when choosing the sampling rate for data collection and registry in pa tient data management systems. This problem compromises most severity of illness scoring sy stems based on data from the \201 rst 24h after ICU admission when using a patient data m anagement system.) Few studies have been published directly comparing the effectiveness of these scores at predicting mortality. More recent models have been shown to be better than their older counterparts. These statistical models are of probabilistic nature. A well cali brated model, applied to an individual patient, may, for example, predict a hospital mortal ity of 48% for this individual; this, however, just means that for a group of 100 patients with a similar severity of illness, 48 patients are predicted to die; it makes no statement of whether the individual patient is included in the 48% who will eventually die or in the 52% who will eventually survive. Thus, applica- tion of these models to individual patient s for decision making is not recommended. Acute Physiology and Chronic Health Evaluation (APACHE) The APACHE systems (versions I \200 \223III) have been described by Knaus in 1981 and there is now version IV. Designed and val idated for use on, or within the \201 rst 24h of, admission and relating the calculated s core during the \201 rst 24h of admission to the vital status at discharge from the hospi tal. They are not developed for repeated use at other time points during the patient \200 \231s ICU stay (note: these scores are at entry to a clinical trial even if this is not at the time of ICU admission). Developed and validated in the USA, the vaildity in other popul ations is questionable. A predictive model for risk- adjusted ICU length of stay is also av ailable (www.criti- caloutcomes.cerner.com). A UK customization is available and was used a s a \201 rst step in the development of the UK Intensive Care National Audit and Research Center (ICNARC) score. Simpli \201 ed Acute Physiology Score (SAPS) The SAPS system, originally from a simpli \201 cation of the APACHE II model, is now a system based on a large multi- national databa se intended to re \202 ect the heterogeneity of current ICU case mix and typology all over the world, with customized equations described for major regions of the world. The origina l SAPS used data from the \201 rst 24h after ICU admission; the last version uses just da ta from the ICU admission ( 1h). For information and free software (allowing the calculati on, storage and export of SAPS 3-related data), see www.saps3.org. Further reading Harrison D, Parry G, Carpenter J, et al. A new risk prediction model: the I ntensive Care National Audit & Research Center (ICNARC) model. Intensive Care Med 2006; 32: S204. Knaus WA, Draper EA, Wagner DP, et al. APACHE II: a severity of disease classi \201 cation system. Crit Care Med 1985; 13: 818 \200\22329. Le Gall JR, Lem eshow S, Saulnier F. A new simpli \201 ed acute physiol- ogy score (SAPS II) based on a Eu ropean/North American multi- center study. JAMA 1993; 270: 2957 \200\22363. Lemeshow S, Teres D, Klar J, et al. Mortality Probability Models (MPM II) based on an inter national cohort of intensive care unit patients. JAMA 1993; 270: 2478 \200\22386. Marshall JC, Cook DA, Christou NV, et al. Multiple organ dysfunc- tion score: a reliable de scriptor of a complex clinical outcome. Crit Care Med 1995; 23: 1638 \200\22352. Mortality Probability Models (MPMs) The MPM II used logistic regression techniques on a lar ge international database of ICU patients There are two scores: the MPM0 or admission model and the MPM24 or 24h model, There are equations to estimate the hospital mortality based o n data from the 48h and the 72h after ICU admission. They are quick and easy to score. MPM II models are no better than SAPS II and their use was always quite restricted. (MPM III ha s been described.) Vincent J-L, Moreno R, Takala J, et al. The SOFA (sepsis-related organ failure assessment) score to describe organ dysfunction/ failure. Intensive Care Med 1996; 22: 707 \200\22310. Zimmerman JE, Kramer AA, McNair DS, et al. Intensive care unit length of stay: benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV. Crit Care Med 2006; 3 4: 2517 \200\22329. Using a severity of illness scoring system The ideal model must have been developed in a si milar population, unless well done validation studies are available. 570 R MORENO & S AFONSO DE CARVALHO Comparison of ICUs Usually, three methods are used to test the adequacy of the model: 1 Calibration: evaluates the degree of correspondence between the estimated probabilities of mortality and the obse rved mortality in the sample under analysis. Four methods have been employed: overall obser ved/ expected mortality ratios, Flora \200\231s Z score, Hosmer \200\223 Lemeshow goodness- of- \201 t tests and calibration curves. 2 Discrimination: how well the model can distingu ish between observations with a positive or a negative out- come. This assessment can be do ne by a non-parametric test such as Harrell \200\231s c-index, using the rank of the mag- n itude of the assessment error. This index measures the probability that for two randomly ch osen patients, the one with the higher probabilistic prediction has the out- come of intere st. It has been shown that this index relates directly to the area under a receiver operati ng characteristic (ROC) curve and can be obtained as the parameter from the Mann \200\223Wh itney \200\223Wilcoxon rank sum test statistic; Intensive care medicine is the science and art of prevent- ing, detecting and managing pati ents at risk or with already established critical illness in order to achieve the best poss ible outcomes of care. It is a complex process, carried out on a heterogeneous patient popu lation and in \202 uenced by several variables that include, but are not limited, to relig ious and cultural background, different structures and organizations of the healthcare syst ems. Major differences in the baseline characteristics of the populations, deter- mined eit her by different genetic characteristics or by different lifestyles, are also important in the \201 nal result of the interaction of the patient with the healthcare system in gener al and with the ICU in particular. This heterogeneity in patient characteristics depends on several factors, related to both the chronic health status and the degree of physiological reserve of the patient, but also on the acute insult or disease responsible for the acute situation and on the timing and characteristics of medical care applied until admission to the ICU. Consequently, any evaluation of the effects of any clinical or non-clinical practi ces in the critically ill patient must take into account the variability in the patient pop ulation characteristics. In other words, when we want to standardize different groups of pa tients, risk adjustment methods allow us to take into account all of the characteristics of patients known to affect their outcome, irrespective of the treat- ment received. This pro cess is called risk adjustment. 3 Uniformity of \201 t: or the performance of the model across major subgroups of patients. The relative importance of calibration and discrimination varies with the utilization that will be given to the model. For research or quality assurance purposes (group comparisons), calibration is especially important; for deci- sions about individual patients both descri ptors are very relevant. From a methodological point of view, the poor calibration of a mod el can be corrected. However, there is almost nothing that can adjust a model when it produ ces poor discrimination. Risk adjustment in the patient with critical illness The risk of death in the patient is ca lculated using general severity scores and general outcome prognostic models (see Chapter 3 3.10). It is important to choose a model that can evaluate the risk of death in all the pat ients under analysis. In this process, several characteristics of the avail- able models sh ould be taken into account: 1 Can all the data needed for computation of the models Computing and interpreting the standardized mortality ratio If the answer to all these poin ts is yes and an appropriate model identi \201 ed, them we can proceed to the next steps o f evaluating the risk-adjusted mortality of the ICU. To achieve that aim, we must then: \200 be collected in a standard and reliable way? 2 Was the handling of data before analysis done according compute the predicted mortality for all patients admit- ted to the ICU during the period un der analysis; count all non-survivals at hospital discharge compute the standardized mortal ity ratio or the ratio of the actual ICU mortality vs the predicted ICU mortality (addition al computations can be done to compute the con \201 dence intervals for this ratio. to the original description of the model? 3 Can the model be used in the large majority of the \200 patients admitted to the ICU under analysis? 4 Does the model takes into account the differences in baseline patient characteristics kno wn to influence mortality? 5 Was the reference population used to develop the model adequately chosen and is the model well cali- brated on that population? As can be seen in Fig. 33.9.1, ICU A presents a standardized mortality ratio signi \201 ca ntly lower than 1. This means that the number of observed deaths was signi \201 cantly low er that the number predicted by the model. In other words, observed mortality, adjusted for the patient characteristics included in the model utilized for risk adjustment, was signi \201 cantly lower than the mortality in the reference database utilized to calibrate the m odel. The reverse is true for ICU F. Other methods have been proposed to compare the ICUs, with several advantages in terms of availability of the information, but more complex and d if \201 cult to use. 6 Is the dimension of the sample under analysis large enough to yield the power for detecti ng significant differences? In this process, the evaluation of the applicability of the general outcome prediction mode l to the population under analysis is possibly the most crucial step. As stated by a recent Consensus Conference \200\230Mortality prediction models are almost always overspeci \201 c for the patient samples upon which they were developed, and thus performance usually deteriorates when models are applied to different population samples \200 . For this reaso n, we recommend that mortality prediction models always be tested in patient samples distin ct from those in which the models were developed \200\231. A word of caution It is quite disputable that ICU performance can be evaluated exclusively by risk-adjusted hospital mortality. 571 CHAPTER 33.11 Comparison of ICUs 2 1,8 1,6 o i t a R y t i l a t r o M d e z i d r a d n a t S 1,4 1,2 1 0,8 0,6 0,4 0,2 0 A B C D E F ICUs Fig. 33.11.1 Comparison of ICUs using a standardized mortality ratio. A \200\223E represent ICUs. Unit A is well below 1, suggesting the mortality in that unit is less than predicted . Conversely, Unit F is well above 1, suggesting a greater mortality than would be predicte d. Other outcomes are certainly important to the patient and to the family (e.g. quality of li fe, long-term outcome) and also for the hospital manager (e.g. costs, effectiveness in the use of resources). Moreover, for quality improve- ment, process indicators are potentially more useful than outcome indicators. Moreno R, Apolone G, Reis Miranda D. Evaluation of the uniformity of \201 t of general ou tcome prediction models. Intensive Care Med 1998; 24: 40 \200\2237. Randall Curtis J, Cook DJ, Wall RJ, et al. Intensive care unit quality improvement: a \200 \230how-to \200\231 guide for the interdisciplinary team. Crit Care Med 2006; 34: 211 \200 \2238. Rapoport J, Teres D, Lemeshow S, et al. A method for assessing the clinical performance and cost-effectiveness of intensive care units: a multicenter inception cohort study. Crit Car e Med 1994; 22: 1385 \200\22391. Further reading Hadorn DC, Keeler EB, Rogers WH, et al. Assessing the perfor- mance of mort ality prediction models. Santa Monica, CA: RAND/ UCLA/Harvard Center for Health Care Financ ing Policy Research, 1993. Schuster DP. Predicting outcome after ICU admission. The art and science of assessing risk. Chest 1992; 102: 1861 \200\22370. Hanley J, McNeil B. The meaning and use of the area under a receiver operating characterist ic (ROC) curve. Radiology 1982; 143: 29 \200\22336. 572 B TAYLOR Critical care disaster planning Introduction A number of recent events have raised awareness of how the demand for critical care beds may rapidly exceed existing capacity. The first of these was the initial Departm ent of Health publication on pandemic in \202 uenza which predicted >50 000 deaths in the UK. As a result of concerns raised by this estimate, and the prediction that signi \201 ca nt numbers of affected patients may require respi- ratory support, the ICS accessed the Cen ter for Disease Control Flusurge modeling program to predict the likely effect on UK intens ive care bed requirements, and con- \201 rmed that existing capacity was likely to be rap idly over- whelmed during the peak of a pandemic. More detailed analysis by the Pandemic In \202 uenza Scienti \201 c Modeling Committee con \201 rmed this prediction, and helped to drive the multi-professional Critical Care Contingency Planning (CCCP) group that was es tablished by the ICS and the Division of Emergency Preparedness, which has worked in conjun ction with the Pandemic In \202 uenza Group to coordinate contingency plans for expanding critical care capacity. Shortly after the CCCP group was inaugurated, the July bombings occ urred in London. Although the number of severely injured survivors who required critical ca re sup- port was lower than might have been anticipated, the impact on several ICUs was dra matic, and many challeng- ing problems had to be overcome, including cancelation of electiv e surgery and the transfer of existing patients. Many of the recommendations that had been included in the draft work undertaken by the CCCP group were enacted spontaneously by the c linicians faced with these problems, even though there had been no prior discussion, sugges ting that the concepts being considered were logical. Later in 2006 the adverse drug trials reactions which led to the simultanous requirement for ICU admission of 6 patients produce d such pressure for beds that a major incident response had to be initiated . Important poi nts and lessons learned \200 could result in sudden excessive demand for critical care admissions. National requirements for NHS organizations to establish contingency plans for an in \202 uenza pandemic provid es additional impetus for contingency planning. Expanding critical care capacity An information-gathering survey obtained responses from 6 0% of UK units, and indicated that Level 2 and Level 3 ICU bed capacity could be escalated by 115 and 83%, respectively, of corresponding baseline totals. These esti- mates were base d on the concepts of expanding critical care facilities into other clinical areas with appr opriate resources (e.g. HDUs, theater recovery areas, coronary care units, etc.), recruitin g the use of all available ventila- tors, monitors and infusion devices, and the cancelatio n of elective surgical procedures to create additional resources and staff availability. Recommendations to expand Level 3 ICU bed capacity \200 Stocktaking should be undertaken to identify all available mechanical ventilators, monitori ng equipment and infu- sion devices, recorded and updated annually. Clinical areas that can be upgraded to provide Level 3 care should be identi \201 ed, and bed numbers calculated. Where new building (or upgrading of existing facilities) is being undertaken, consideratio n should be given to plan- ning for the inclusion of piped air, oxygen and appropri- ate po wer supplies to allow capacity expansion If specialist units (e.g. cardiothoracic or neuros urgical ICUs) are available locally the planning process should include these resources whi lst preserving sufficient capacity for urgent surgical procedures Planning strategies shoul d also involve any independent sector facilities that might be able to provide \200\230ste p down \200\231 Level 2 care or provide support in other ways. Plans for transferring patie nts to other critical care units (or ward areas where appropriate) should be made, even tho ugh they are only likely to be of bene \201 t if the increased demand is limited to a loca l geographical area. The CCCP guidance has suggested that a target level of 100% escalation of existing Level 3 capacity should be set. Although this \201 gure exceeds the national survey estimates, a number of NHS units which have carried out their own planning exercise s have indicated that expansion to this level (and in some instances consider- ably greater ) is feasible. If/when a disaster event occurs, it is likely that existing critical care beds will already be occupied Professional responses of critical care staff are likely to result in extraord inary measures to try to ensure that patients receive prompt and appropriate treatment Sust aining such responses puts staff under considerable pressure, and team-supporting strategie s are essential Equipment availability and supplies may be a problem The nature of the even t may lead to previously unfamiliar and unpredictable injury patterns that can pose sign \201 - cant challenges to critical clinicians and nursing staff Identifying patients and ne xt of kin may be dif \201 cult Support of neighboring units/specialist units is crucial Th e impact on elective surgery, waiting times and other patient services is likely to be sign i \201 cant \200 Staf \201 ng Critical care capacity expansion is more likely to be limited by staff availa bility than by equipment. One study suggests that, depending on the cause of the incident, up to 40% of healthcare staff may not be available to provide patient care. Infection/toxic events are more likely to produce staff absence; events that cause high levels of injury o r trauma are more likely to be well attended. Staff availability may be compromised because of: \200 \200 \200 \200 \200 \200 \200 As a result of these lessons, and other work undertaken to plan for future eventualities, r ecommendations for critical care services have been made. Some of these may be more aspirat ional than achievable, and variations in infrastruc- tures may mean that local solutions ma y result in a better likelihood of services coping. It is nevertheless important that all u nits should give consideration to expanding capacity, as even a relatively minor local even t (e.g. train crash) Staff being affected/infected/injured by the event Need to provide care for children or rel atives Transportation availability Fear of disease transmission (to staff or their family) Communications failure 573 CHAPTER 33.12 Critical care disaster planning networking will help to manage a surge of demand, it is less likely that treatment limitati on decisions may have to be made. In a national crisis, however, where all critical care fa cilities may become rapidly overwhelmed, staff will be faced with very dif \201 cult decis ions on which patients should receive mechanical ventilation and on escalation of treat- me nt levels. Although recommended ethical principles have been made for pandemic in \202 uen za, concerns remain that these may not help clinicians faced with the reality of too many p atients and not enough beds. Decisions to withhold or withdraw treatment that would not be consid- ered in normal circumstances because of lack of resources may also have signi \201 cant implications for intensivists. To address these concerns an extensive consultation pr ocess has been followed to produce guidance which emphasizes the importance of consistency of healthcare restrictions from primary to advanced secondary care, and includes triaging c riteria based on the SOFA scoring system initially published by Canadian intensivists. Alth ough the final Surge Capacity & Prioritization document version is likely to help with many of the concerns raised, it also remains probable that in severe circumstances even more st ringent triaging may be necessary because of the lower numbers of critical care beds per he ad of population in the UK. Accordingly local decisions on treatment restrictions \200\224 including the possibility of temporary ICU closure to new admissions \200\224may have to be agreed with trust senior management and SHAs, the reasons being carefully docu- mented and reviewed on a daily basis. Principles to consider \200 Operating theater staff and those who work in areas such as HDUs or coronary care units are likely to be the most able to assist as reserve critical care staff. Trained critical care nurses (CCNs) should be able to oversee the care of several patients supported by reserve staff; the ratio of trained CCN to reserve staff/ patients will depend on the severity of t he event. Training programs need to be developed to update reserve staff on the principles of critical care support; these should be maintained on a rolling basis. Teaching programs should also be established to train staff in the use of personal protection equipment, infe c- tion control procedures, and other issues that relate to staff and patient safety. Robus t mechanisms for support of staff morale should be developed; reassuring con \201 dence an d promoting teamworking will help to maintain staf \201 ng levels. Reductions/cancelation of elective surgery will mean that anesthesia consultants and trainees may be able to assis t in critical care expansion. The recruitment of senior staff/trainees from other clinical specialties may be feasible in the future as more trainees complete training modules in cri tical care. Basic critical care training programs are also available online. Standards of care Although the aim should always be to maintain high stand- ards of care, t he need for pragmatism must be recognized. As the number of patients requiring mechanical v entilatory support rises and staf \201 ng ratios become more dif \201 cult, it is inevita ble that the risks of adverse events will increase. When resources are signi \201 cantly o verstretched, inter- ventions normally considered appropriate may not be achievable because of availability of staff, equipment or consumables. Staff who undertake responsibility for care of patients or procedures that are outside of their normal working practice need to h ave strong reassurance that they will not be vulnerable to professional criticism or litiga tion for doing the best that they can under dif \201 cult circumstances. Until this concep t is formally supported nationally, the responsibility for providing adequate reassurance a nd support for staff must lie with local trust management and SHAs. Pediatrics Dealing with a signi \201 cant number of infants and children who require criti cal care support will pose a major challenge. Pediatric ICUs already run at high levels of capacity and, even if escalation plans are enacted, will have limited ability to cope with increased demand. A local major incident could also produce the requirement for a number of children to receive critical care stabilization procedures prior to transfer to other cent ers. This reinforces the importance of preserving pediatric stabilization expertise in all hospitals where children may be admitted. Further reading Christian MD, Hawryluck, L, Wax RS, et al. Development of a triage protocol for critical care during an in \202 uenza pandemic. CMAJ 2006; 175: 1377 \200\22381. http://www.aic.cuhk.edu.hk/web8/BASIC.htm http://www.dh.gov.uk/en/Publicationsandstatist ic s/Publications/PublicationsPolicyAndGuidanc e/DH_073179 Supplies and disposables Pandemic in \202 uenza planning has led to recognition that most NHS institutions depend on a \200\230just in time \200\231 supply system, and that mechani sms have to be re \201 ned to ensure that supply chains can be maintained during a major i nci- dent. Although a considerable amount of work has been put into this, it remains far fr om certain that supply systems will be suf \201 ciently robust to maintain essential servi ces, particularly as demand for some items may signi \201 cantly exceed normal requirement s. Cooperative networking arrangements will help units to cope with a local or even a regio nal major incident, but an event which results in simul- taneous increased demand for suppl ies will pose a major challenge. Menon DK, Taylor BL, Ridley SA. The Intensive Care Society, UK. Modeling the impact of an i n \202 uenza pandemic on critical care services in England. Anesthesia 2005; 60: 952 \200 \2234. Qureshi K, Gershon RRM, Sherman MF et al. Health care workers \200\231 ability and willingn ess to report to duty during catastrophic d isasters. Bull NY Acad Med 2005; 82: 378 \200 \22388. Suntharalingam G, Perry MR, Ward S, et al. Cytokine storm in a Phase 1 trial of the anti-CD 28 monoclonal antibody TGN1412. N Engl J Med 2006; 355: 1018 \200\22328. UK Department of Health. UK Health Department \200\231s in \202 uenza pandemic contingency plan. London: DoH, 2005. www.dh.gov.uk/prod_consum_dh/groups/dh_digita- lasets/@dh/@en/documents/digitalasset/ dh_41 37619.pdf (2006) Critical Care Contingency Planning. Triaging and ethics These are the most complex problems that critical care staff may face, and many issues remain unresolved. In smaller incidents where local/regional expansion and 574 M JONAS & CS WALDMANN Health technology assessment De \201 nition \200\230Health technologies \200\231 are broadly de \201 ned to include a ll interventions used to promote health, prevent and treat disease, and improve rehabilitat ion and long-term care. This may include drugs, devices, procedures (surgical tech- niques, screening and counseling) and even the settings of care, such as general practice, hospita ls and care homes. process will be of bene \201 t or be safe in clinical use. In the USA, the FDA ful \201 l s a similar role but, in addition, before granting approval for sale in the USA, has a remi t beyond the technical quali \201 cations of the CE mark and mandates some assessment of c linical operation. The FDA uses clini- cal experts to review investigational studies relati ng to the device to ascertain whether the product does what is claimed by the manufacturer and/or presents unreasonable risks to the patient. The FDA classify devices into three clas ses according to the risk of potential harm to the user, ranging from Class 1 devices that present minimal risk to the user, e.g. home blood pressure kits, to Class 3, e.g. dialysis machines or ICU ventilators Background The NHS Technology Assessment Program was set up in 1993, and the \201 ndings and output from this Program directly in \202 uence key decision-making bodies such as the NICE. Although the de \201 nition of health technology assess- ment (HTA) presents a wide spectrum of interventions, with the exception of drug based technologies, most of the prin ciples involved can be considered with reference to medical devices. The drug-based technol ogies and pharmaceutical industry are tightly legislated and require assessments ranging fr om animal studies to large interventional clinical trials. In contrast, medical devices hav e relatively little legislature surrounding clinical introduction, and there frequently may not be a consensus as to what constitutes adequate assessment. There is as a result a need for further develop- ments of the science relating to HTA. This is especially pertinent wh en consideration of the escalating numbers of devices for which manufacturers make claims r elating to patient care. The ensuing HTA exercises stipulate study design and assessment of these new technologies in an attempt to judge and evaluate short- and long-term bene \201 ts. In effect, there are two questions to answer: does the device have a positive clinical impact and are the resultant economics affordable in the context of local/national healthc are funding? The requirement for a balanced approach of science vs economics ensures that t he assessment design will draw on expertise from clinicians, engineers, statisticians, econ omists, ethicists and of course lawyers. Device performance Ef \201 cacy and effectiveness are used to assess device per- formance. The ef \201 cacy relates to the device performance under ideal conditions, and the effect iveness is the per- formance under normal clinical working conditions. This is an important distinction as ef \201 cacy ratings may indicate the necessity for \200\230experts \200 \231 to operate the device, whereas effectiveness relates to the performance limitations un der average clinical conditions. Overall, when reviewing tech- nology, the new approach sho uld offer improvement over current technologies. This may be because the device can obtain measurements with less risk to the patient, i.e. is less invasive, or has some other aspect which is advanta- geous. Ef \201 cacy Assessing ef \201 cacy usually means comparison of the device against a know n \200\230gold \200\231 standard. When the measurement is well de \201 ned, e.g. cardiac output, the ef \201 cacy becomes the mathematical correlate of accuracy. Accuracy is the c loseness of the device measurement to the actual value and can be described by: \200 \200 \200 Correlation (degree of association between readings) Precision (the closeness of repeated m easurements) Bias (the difference between the means of the measurements)