Accuracy of surgical complication rate estimation using ICD-10 codes.

BACKGROUND: The ICD-10 codes are used globally for comparison of diagnoses and complications, and are an important tool for the development of patient safety, healthcare policies and the health economy. The aim of this study was to investigate the accuracy of verified complication rates in surgical admissions identified by ICD-10 codes and to validate these estimates against complications identified using the established Global Trigger Tool (GTT) methodology. METHODS: This was a prospective observational study of a sample of surgical admissions in two Norwegian hospitals. Complications were identified and classified by two expert GTT teams who reviewed patients' medical records. Three trained reviewers verified ICD-10 codes indicating a complication present on admission or emerging in hospital. RESULTS: A total of 700 admissions were drawn randomly from 12 966 procedures. Some 519 possible complications were identified in 332 of 700 admissions (47·4 per cent) from ICD-10 codes. Verification of the ICD-10 codes against information from patients' medical records confirmed 298 as in-hospital complications in 141 of 700 admissions (20·1 per cent). Using GTT methodology, 331 complications were found in 212 of 700 admissions (30·3 per cent). Agreement between the two methods reached 83·3 per cent after verification of ICD-10 codes. The odds ratio for identifying complications using the GTT increased from 5·85 (95 per cent c.i. 4·06 to 8·44) to 25·38 (15·41 to 41·79) when ICD-10 complication codes were verified against patients' medical records. CONCLUSION: Verified ICD-10 codes strengthen the accuracy of complication rates. Use of non-verified complication codes from administrative systems significantly overestimates in-hospital surgical complication rates.

Attitudes of physicians towards the care of critically ill elderly patients - a European survey.

BACKGROUND: Very elderly patients are one of the fastest growing population in ICUs worldwide. There are lots of controversies regarding admission, discharge of critically ill elderly patients, and also on treatment intensity during the ICU stay. As a consequence, practices vary considerably from one ICU to another. In that perspective, we collected opinions of experienced ICU physicians across Europe on statements focusing on patients older than 80. METHODS: We sent an online questionnaire to the coordinator ICU physician of all participating ICUs of an recent European, observational study of Very old critically Ill Patients (VIP1 study). This questionnaire contained 12 statements about admission, triage, treatment and discharge of patients older than 80. RESULTS: We received answers from 162 ICUs (52% of VIP1-study) spanning 20 different European countries. There were major disagreements between ICUs. Responders disagree that: there is clear evidence that ICU admission is beneficial (37%); seeking relatives' opinion is mandatory (17%); written triage guidelines must be available either at the hospital or ICU level (20%); level of care should be reduced (25%); a consultation of a geriatrician should be sought (34%) and a geriatrician should be part of the post-ICU trail (11%). The percentage of disagreement varies between statements and European regions. CONCLUSION: There are major differences in the attitude of European ICU physicians on the admission, triage and treatment policies of patients older than 80 emphasizing the lack of consensus and poor level of evidence for most of the statements and outlining the need for future interventional studies.

An observational study of community-acquired severe sepsis comparing intensive care and non-intensive care patients.

BACKGROUND: Most studies of sepsis are from intensive care units (ICUs). We aimed to investigate community-acquired severe sepsis in a broader population, in order to compare patients treated in or outside an ICU . METHODS: We performed a 1-year prospective observational study with enrollment of patients from three units; a general ICU, a combined ICU/non-ICU and a medical ward with limited surveillance facilities. Hospital survivors were followed up for 5 years. RESULTS: Overall, 220 patients were included, of which 107 received ICU treatment. The majority of abdominal (77%, P = 0.003) and genitourinary (81%, P < 0.001) infections were found in ICU and non-ICU patients, respectively. Time to first antibiotic administration was longer in ICU-patients (median 3.5 vs. 2.0 h in non-ICU patients, P = 0.011). ICU developed more organ dysfunctions than non-ICU patients (P < 0.001), nevertheless supportive therapy with vasoactive drugs and non-invasive ventilation was documented in 22% and 27% of the latter. Median hospital length of stay was 15 vs. 9 days (P = 0.001), and hospital and 5-year mortality rates 35% vs. 16% (P = 0.002) and 57% vs. 58% (P = 0.892) among ICU and non-ICU patients, respectively. Increasing age (HR 1.06 (1.04, 1.07) per year, P < 0.001), not care level during hospitalization (HR 1.19 (0.70, 2.02), P = 0.514), influenced long-term survival. CONCLUSION: Half of the subjects with community-acquired severe sepsis never received ICU treatment. Still, use of organ supportive therapy outside the ICU was considerable. Hospital mortality was higher, whereas 5-year survival was similar when comparing ICU with non-ICU patients.

Great variation between ICU physicians in the approach to making end-of-life decisions.

INTRODUCTION: End-of-life (EOL) decision-making in the intensive care unit (ICU) is difficult, but is rarely practiced in simulated settings. We wanted to explore different strategies ICU physicians use when making EOL decisions, and whether attitudes towards EOL decisions differ between a small-group simulation setting and a large-group plenary setting. METHODS: The study took place during a Scandinavian anaesthesiology and intensive care conference. The simulated ICU patient had a cancer disease with a grave prognosis, had undergone surgery, suffered from severe co-morbidities and had a son present demanding all possible treatment. The participants were asked to make a decision regarding further ICU care. We presented the same case scenario in a plenary session with voting opportunities. RESULTS: In the simulation group (n = 48), ICU physicians used various strategies to come to an EOL decision: patient-oriented, family-oriented, staff-oriented and regulatory-oriented. The simulation group was more willing than the plenary group (n = 47) to readmit the patient to the ICU if the patient again would need respiratory support (32% vs. 8%, P < 0.001). Still, fewer participants in the simulation group than in the plenary group (21% vs. 38%, P = 0.019) considered the patient's life expectancy of living an independent life to be over 10%. CONCLUSION: There was great variation between ICU physicians in the approach to making EOL decisions, and large variations in their life expectancy estimates. Participants in the simulation group were more willing to admit and readmit the patient to the ICU, despite being more pessimistic towards life expectancies. We believe simulation can be used more extensively in EOL decision-making training.

Cognitive effects of hospital calls in anaesthesiologists.

BACKGROUND: The work hours of Norwegian physicians are under scrutiny because of an increased public focus on patient safety. Ample international research indicate harmful effects of doctor fatigue based on studies on physicians working long weeks and on-call shifts of more than 30 consecutive hours. There is a lack of research on effects relevant for short or intermediate length of work weeks and call shifts. This study intended to study cognitive effects of short or intermediate duration in-hospital calls. METHODS: Eighteen anaesthesiology residents working on-call at an operation ward or an intensive care unit at Haukeland University Hospital were invited to participate. Schedules were adapted to allow for two additional experimental shifts. Participants were subjected to Cambridge Neuropsychological Test Automated Battery cognitive testing in a rested state and on three occasions after call. Amount of sleep and self-assessed sleepiness were recorded. RESULTS: Ten residents completed all four tests during 10 months. Reaction time was longer post-call. It was significantly increased only after the 18 h night call, by 21.1 and 20.5 ms for simple and five-choice reaction time, respectively. Executive function was not significantly altered post-call. Visual memory was improved post-call. Karolinska Sleepiness Score was increased by 3.3 (long day), 2.1 (short night) and 2.5 (long night) points post-call. CONCLUSION: Reaction times were increased after 18 h night calls and non-significant increases in reaction times were apparent after the other on-call shifts. Self reported sleepiness was increased post-call. We were not able to conclude whether executive function or memory was negatively affected post-call.

A calibration study of SAPS II with Norwegian intensive care registry data.

BACKGROUND: Mortality prediction is important in intensive care. The Simplified Acute Physiology Score (SAPS) II is a tool for predicting such mortality. However, the original SAPS II is poorly calibrated to current intensive care unit (ICU) populations because it draws on data, which is more than 20 years old. We aimed to improve the calibration of SAPS II using data from the Norwegian Intensive Care Registry (NIR). This is the first recalibration of SAPS II for Nordic data. METHODS: A first-level customization was applied to improve calibration of the original SAPS II model (Model A). NIR data used covered more than 90% of adult patients admitted to ICUs in Norway from 2008 to 2010 (n = 30712). RESULTS: The modified SAPS II, Model B, outperformed the original Model A with respect to calibration. Model B gave more accurate predictions of mortality than Model A (Hosmer-Lemeshow's C: 22.01 vs. 689.07; Brier score: 0.120 vs. 0.131; Cox's calibration regression: α = -0.093 vs. -0.747, ß = 0.921 vs. 0.735, (α|ß = 1) = -0.009 vs. -0.630). The standardized mortality ratio was 0.73 [95% confidence interval (CI) of 0.70-0.76] for Model A and 0.99 (95% CI of 0.95-1.04) for Model B. Discrimination was good for both models (area under receiver operating characteristic curve = 0.83 for both models). CONCLUSIONS: As expected, Model B is better calibrated than Model A, and both models have similar uniformity of fit and equal discrimination. Introducing Model B into Norwegian ICUs may improve precision in decision-making. Units will have a more realistic benchmark for the assessment of ICU performance. Mortality risk estimates from Model B are better than previous SAPS II estimates have been.

The present use of quality indicators in the intensive care unit.

Quality indicators (QIs) are increasingly used in medicine in order to compare and eventually to improve quality of delivered health care. During the last decade, QIs also have been used within intensive care medicine. This paper shortly describes this development and gives an overview of QIs in the intensive care unit (ICU) reported to be in use at national level. Using a search on PubMed and through World Wide Web, QIs documented to be in use at a national level were retrieved. The various sets of QI were compared, and the method to select QIs was found. The search retrieved national indicators from eight countries (United Kingdom, the Netherlands, Spain, Sweden, Germany, Scotland, Austria and India). A total of 63 QIs were in use, and no single indicator was common for all countries. The most frequently used indicator was the standardised mortality rate (in six of eight countries). Measurements of patient/family satisfaction, the presence of an ICU specialist 24/7 and the occurrence of ventilator-associated pneumonia were all used by five countries. All primarily used a physician-driven process to select national QIs. This survey reveals that the concept of QIs is perceived differently throughout countries, also within developed countries in Western Europe. At present, it will be difficult to use national QIs to compare the quality of intensive care between countries.

Cognitive impairments after critical illness.

BACKGROUND: Cognitive impairments are common after critical illness. Aetiology and effects of cognitive impairments in this setting are not fully revealed. The aim of this study was to investigate the effect of critical illness and intensive care unit (ICU) treatment on cerebral function. METHODS: Adult ICU patients with no previous history of cerebral disorders were included. Non-delirious patients scoring ≥ 24 on mini-mental state examination on ICU discharge were explored neuropsychologically using the Cambridge Neuropsychological Test Automated Battery (CANTAB) to classify cognitive impairments. Tests were repeated at 3 and 12 months. Results were compared with a normal reference population and a surgical comparison group. RESULTS: We included 55 patients. Eighteen of 28 patients were cognitively impaired, and it was not possible to classify 27 patients. The ICU survivors tested with CANTAB scored significantly lower than the reference population. They also scored worse than a surgical comparison group but significantly on only one of 10 measures. At 3 months follow-up, included patients scored significantly worse on one of 10 reported CANTAB measures. There were no differences at 12 months. We found no associations between age, co-morbidity, Simplified Acute Physiology Score II, Sequential Organ Failure Assessment score, presence of cardiovascular disease, duration of ventilatory support and length of ICU stay, and cognitive impairments. Having a cognitive impairment did not affect other outcome measures such as mortality, health-related quality of life, and institutionalization. CONCLUSIONS: Cognitive impairments are common after critical illness and may be caused by the critical illness in itself. Incidences are high after ICU discharge (64%) but drops rapidly during the first 3 months after discharge.

Cognitive dysfunction and health-related quality of life after a cardiac arrest and therapeutic hypothermia.

BACKGROUND: Evidence-based treatment protocols including therapeutic hypothermia have increased hospital survival to over 50% in unconscious out-of-hospital cardiac arrest survivors. In this study we estimated the incidence of cognitive dysfunctions in a group of cardiac arrest survivors with a high functional outcome treated with therapeutic hypothermia. Secondarily, we assessed the cardiac arrest group's level of cognitive performance in each tested cognitive domain and investigated the relationship between cognitive function and age, time since cardiac arrest and health-related quality of life (HRQOL). METHODS: We included 26 patients 13-28 months after a cardiac arrest. All patients were scored using the Cerebral Performance Category scale (CPC) and Mini-Mental State Examination (MMSE). Twenty-five of the patients were tested for cognitive function using the Cambridge Neuropsychological Test Automated Battery (CANTAB). These patients were tested using four cognitive tests: Motor Screening Test, Delayed Matching to Sample, Stockings of Cambridge and Paired Associate Learning from CANTAB. All patients filled in the Short Form-36 for the assessment of HRQOL. RESULTS: Thirteen of 25 (52%) patients were classified as having a cognitive dysfunction. Compared with the reference population, there was no difference in the performance in motor function and delayed memory but there were significant differences in executive function and episodic memory. We found no associations between cognitive function and age, time since cardiac arrest or HRQOL. CONCLUSION: Half of the patients had a cognitive dysfunction with reduced performance on executive function and episodic memory, indicating frontal and temporal lobe affection, respectively. Reduced performance did not affect HRQOL.

Impact of the post-World War II generation on intensive care needs in Norway.

BACKGROUND: A high birth rate during the first two decades following World War II has increased the proportion of elderly people in present-day society and, consequently, the demand for health-care services. The impact on intensive care services may become dramatic because the age distribution of critically ill patients is skewed towards the elderly. We have used registry data and population statistics to forecast the demand for intensive care services in Norway up until the year 2025. METHODS: Data collected by the Norwegian intensive care registry (NIR), showing the age distribution in Norwegian intensive care units (ICU) during the years 2006 and 2007, were used with three different Norwegian prognostic models of population growth for the years 2008-2025 to compute the expected increase in intensive care unit bed-days (ICU bed-days). RESULTS: The elderly were overrepresented in Norwegian ICUs in 2006-2007, with patients from 60 to 79 years of age occupying 44% of ICU bed-days. Population growth from 2008 to 2025 was estimated to be from 11.1 to 26.4%, depending on the model used. Growth will be much larger in the age group 60-79 years. Other factors kept unchanged, this will result in an increase in the need for intensive care (ICU bed-days) of between 26.1 and 36.9%. CONCLUSION: The demand for intensive care beds will increase markedly in Norwegian hospitals in the near future. This will have serious implications for the planning of infrastructure, education of health care personnel, as well as financing of our health care system.

A comparison of SAPS II and SAPS 3 in a Norwegian intensive care unit population.

BACKGROUND: Simplified Acute Physiology Score (SAPS II) is the most widely used general severity scoring system in European intensive care medicine. Because its performance has been questioned in several external validation studies, SAPS 3 was recently released. To our knowledge, there are no published validation studies of SAPS II or SAPS 3 in the Scandinavian countries. We aimed to evaluate and compare the performance of SAPS II and SAPS 3 in a Norwegian intensive care unit (ICU) population. METHOD: Prospectively collected data from adult patients admitted to two general ICUs at two different hospitals in Norway were used. Probability of mortality was calculated using the SAPS 3 global equation (SAPS 3 G), the SAPS 3 Northern European equation (SAPS 3 NE), and the original SAPS II equation. Performance was assessed by the standardized mortality ratio (SMR), area under receiving operating characteristic, and the Hosmer and Lemeshow goodness-of-fit C test. RESULTS: One thousand eight hundred and sixty-two patients were included after excluding readmissions, and patients who were admitted after coronary surgery or burns. The SMRs were SAPS 3 G 0.71 (0.65, 0.78), SAPS 3 NE 0.74 (0.68, 0.81), and SAPS II 0.82 (0.75, 0.91). Discrimination was good in all systems. Only the SAPS 3 equations displayed satisfactory calibration, as measured by the Hosmer-Lemeshow test. CONCLUSION: The performance of SAPS 3 was satisfactory, but not markedly better than SAPS II. Both systems considerably overestimated mortality and exhibited good discrimination, but only the SAPS 3 equations showed satisfactory calibration. Customization of these equations based on a larger cohort is recommended.

Severity scoring in the ICU: a review.

BACKGROUND: Patients in the intensive care unit (ICU) require huge resources because of the dysfunction of several of their vital organs. The heterogeneity and complexity of the ICU patient have generated interest in systems able to measure severity of illness as a method of predicting outcome, comparing quality-of-care and stratification for clinical trials. METHODS: By searching Medline and EMBASE for publications describing scoring systems in the ICU, the most frequently used systems, defined as resulting in more than 50 references, are included in this review. Scoring systems belong to one of four classes prognostic, single-organ failure, trauma scores and organ dysfunction (OD). The different systems are described and discussed. RESULTS: Three different prognostic scoring systems, including several versions, four single OD scores and three OD scores, were included in this review. CONCLUSION: Different forms of scoring systems are frequently used in the ICU. They have become a necessary tool to describe ICU populations and to explain differences in mortality. As there are several pitfalls related to the interpretation of the numbers supplied by the systems, they should not be used without knowledge on the science of severity scoring.