Long-term prognosis for patients admitted to a medical intermediate care unit. / Langtidsprognose for pasienter innlagt i en medisinsk overvåkningsavdeling.

BACKGROUND: The intermediate care unit at Akershus University Hospital treats patients with incipient or manifest organ failure. Selecting patients who might benefit from treatment in an intermediate care unit is challenging. Few data are available on long-term survival of patients treated in medical intermediate care units and on how assumed favourable and unfavourable prognostic factors predict long-term survival in this population. MATERIAL AND METHOD: Comorbidity, reason for admission and whether an infection was a direct or contributory reason for the admission were prospectively registered for patients in the unit in 2014 and 2016. We registered mortality up to six years after the admission and conducted a logistic regression analysis with three-year survival as the outcome variable. RESULTS: Of the 2 170 included patients, 153 (7 %) died in the intermediate care unit. Of the 2 017 patients who were discharged alive from the intermediate care unit, 55 % were still alive three years later, including 28 % of older patients aged over 80 years and 23 % of patients with cancer. Age, malignancy, other comorbidity and infection were predictors of mortality. INTERPRETATION: Many patient groups in an intermediate care unit have a poor long-term prognosis. However, people older than 80 years, cancer patients or patients with another serious comorbidity may live long after their stay in an intermediate care unit, and the fact of belonging to these groups should not be an independent reason for withholding treatment.

Epidemiology and prognoses in a medical intermediate care unit. / Epidemiologi og prognoser i en medisinsk overvåkningsavdeling.

BACKGROUND: The purpose of medical intermediate care units is the observation and treatment of patients with incipient or manifest organ failure. We wished to obtain data on which conditions result in admission to these units and the prognosis for these patients. MATERIAL AND METHOD: All patients admitted to the medical intermediate care unit at Akershus University Hospital in 2014 were registered prospectively with reason for admission, period of hospitalisation, degree of severity, comorbidity, last place of hospitalisation prior to medical intermediate care and treatment limitations (do-not-resuscitate order and/or do-not-intubate order). Mortality in the hospital and one year after hospitalisation were registered retrospectively. Multiple regression analysis was performed with hospital mortality as the outcome variable. RESULTS: Altogether 1369 patient hospitalisations for 1118 unique patients were included. The most frequent reasons for admission were pneumonia, chronic obstructive pulmonary disease, sepsis, poisonings and hyponatraemia. The degree of severity of the condition for which patients were admitted corresponded to that reported by intensive care departments in Norwegian local hospitals. A total of 13 % died during their stay in hospital and a further 14 % in the course of one year. The highest mortality was for patients with severe infection, cardiac failure and restrictive/neuromuscular respiratory disorder. The degree of severity, age, infection, comorbidity and ward as admitting unit were predictors of mortality during the hospitalisation period. Risk-adjusted mortality ratio of 0.64 satisfied the quality objective for intensive care departments (<0.7). A total of 5.6 % of hospitalisations in the medical intermediate care unit entailed transfer to the intensive care ward. INTERPRETATION: The degree of severity of the condition for which patients were admitted was high, and the treatment outcomes judged upon expected mortality were good. Medical intermediate care units can relieve pressure on wards with seriously ill patients without taking up intensive care beds.

[Mortality surveillance in an intensive care unit]. / Overvåking av dødelighet i en intensivavdeling.

BACKGROUND: Annual reports from Norwegian Intensive Care Units include risk-adjusted mortality data. Annual variation may be difficult to interpret due to random variation and changes in case mix. Several methods have been developed for continuous monitoring of treatment results in clinical practices where it is possible to calculate a risk for a certain outcome, usually risk of death. We have used the cumulative risk adjusted mortality chart Variable Life Adjusted Display (VLAD) to further illustrate our treatment results. MATERIAL AND METHODS: 3190 patients were treated in our intensive care unit during the period 1997-2006. 2777 of these patients were given scores for risk of death during hospitalization and the treatment result (as risk-adjusted mortality) was calculated. RESULTS: The risk-adjusted mortality varied from year to year with a tendency for better survival the last years. Variation in mortality was clearly demonstrated by the Variable Life Adjusted Display (VLAD) curve. INTERPRETATION: The VLAD curve is a very useful supplement to traditional methods of evaluating the performance of intensive care units. The method is useful for showing how treatment results vary over time.

Charlson comorbidity index derived from chart review or administrative data: agreement and prediction of mortality in intensive care patients.

PURPOSE: This study compared the Charlson comorbidity index (CCI) information derived from chart review and administrative systems to assess the completeness and agreement between scores, evaluate the capacity to predict 30-day and 1-year mortality in intensive care unit (ICU) patients, and compare the predictive capacity with that of the Simplified Acute Physiology Score (SAPS) II model. PATIENTS AND METHODS: Using data from 959 patients admitted to a general ICU in a Norwegian university hospital from 2007 to 2009, we compared the CCI score derived from chart review and administrative systems. Agreement was assessed using % agreement, kappa, and weighted kappa. The capacity to predict 30-day and 1-year mortality was assessed using logistic regression, model discrimination with the c-statistic, and calibration with a goodness-of-fit statistic. RESULTS: The CCI was complete (n=959) when calculated from chart review, but less complete from administrative data (n=839). Agreement was good, with a weighted kappa of 0.667 (95% confidence interval: 0.596-0.714). The c-statistics for categorized CCI scores from charts and administrative data were similar in the model that included age, sex, and type of admission: 0.755 and 0.743 for 30-day mortality, respectively, and 0.783 and 0.775, respectively, for 1-year mortality. Goodness-of-fit statistics supported the model fit. CONCLUSION: The CCI scores from chart review and administrative data showed good agreement and predicted 30-day and 1-year mortality in ICU patients. CCI combined with age, sex, and type of admission predicted mortality almost as well as the physiology-based SAPS II.

Long-range rescue helicopter missions in the Arctic.

BACKGROUND: Search and rescue helicopters from the Royal Norwegian Air Force conduct ambulance and search and rescue missions in the Barents Sea. The team on-board includes an anesthesiologist and a paramedic. Operations in this area are challenging due to long distances, severe weather conditions, and arctic winter darkness. METHODS: One-hundred, forty-seven ambulance and 29 search and rescue missions in the Barents Sea during 1994-1999 were studied retrospectively with special emphasis on operative conditions and medical results. RESULTS AND DISCUSSION: Thirty-five percent of the missions were carried out in darkness. The median time from the alarm to first patient contact was 3.3 hours and the median duration of the missions was 7.3 hours. Forty-eight percent of the missions involved ships of foreign origin. Half the patients had acute illnesses, dominated by gastrointestinal and heart diseases. Most of the injuries resulted from industrial accidents with open and closed fractures, amputations, and soft tissue damage. Ninety percent of the patients were hospitalized; 7.5% probably would not have survived without early medical treatment and rapid transportation to a hospital. CONCLUSION: Using a heavy search and rescue helicopter in the Barents Sea was the right decision in terms of medical gain and operative risk.

Nosocomial candidemia; risk factors and prognosis revisited; 11 years experience from a Norwegian secondary hospital.

The aim of the study was to review the epidemiology and prognosis of candidemia in a secondary hospital, and to examine the intra-hospital distribution of candidemia patients. Study design is a retrospective cohort study. Trough 2002-2012, 110 cases of candidemia were diagnosed, giving an incidence of 2, 6/100,000 citizens/year. Overall prognosis of candidemia was dismal, with a 30 days case fatality rate of 49% and one year case fatality rate of 64%. Candidemia was a terminal event in 55% of 30 days non-survivors, defined as Candida blood cultures reported positive on the day of death or thereafter (39%), or treatment refrained due to hopeless short-term prognosis (16%). In terminal event candidemias, advanced or incurable cancer was present in 29%. Non-survivors at 30 days were 9 years (median) older than survivors. In 30 days survivors, candidemia was not recognised before discharge in 13% of cases. No treatment were given and no deaths or complications were observed in this group. Candidemia patients were grouped into 8 patient categories: Abdominal surgery (35%), urology (13%), other surgery (11%), pneumonia (13%), haematological malignancy (7%), intravenous drug abuse (4%), other medical (15%), and new-borns (3%). Candidemia was diagnosed while admitted in the ICU in 46% of patients. Urology related cases were all diagnosed in the general ward. Multiple surgical procedures were done in 60% of abdominal surgery patients. Antibiotics were administered prior to candidemia in 87% of patients, with median duration 17 (1-108) days. Neutropenia was less common than expected in patients with candidemia (8/105) and closely associated to haematological malignancy (6/8). Compared with previous national figures the epidemiology of invasive candidiasis seems not to have changed over the last decade.