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The burden of critical illness among adults in a Swedish region—a population-based point-prevalence study

European Journal of Medical Research volume 28, Article number: 322 (2023) Cite this article

Abstract

Background

Patients with critical illness have a high risk of mortality. Key decision-making in the health system affecting the outcomes of critically ill patients requires epidemiological evidence, but the burden of critical illness is largely unknown. This study aimed to estimate the prevalence of critical illness in a Swedish region. Secondary objectives were to estimate the proportion of hospital inpatients who are critically ill and to describe the in-hospital location of critically ill patients.

Methods

A prospective, multi-center, population-based, point-prevalence study on specific days in 2017–2018. All adult (> 18 years) in-patients, regardless of admitting specially, in all acute hospitals in Sörmland, and the patients from Sörmland who had been referred to university hospitals, were included. Patients in the operating theatres, with a psychiatric cause of admission, women in active labor and moribund patients, were excluded. All participants were examined by trained data collectors. Critical illness was defined as “a state of ill health with vital organ dysfunction, a high risk of imminent death if care is not provided and a potential for reversibility”. The presence of one or more severely deranged vital signs was used to classify critical illness. The prevalence of critical illness was calculated as the number of critically ill patients divided by the number of adults in the region.

Results

A total of 1269 patients were included in the study. Median age was 74 years and 50% of patients were female. Critical illness was present in 133 patients, resulting in an adult population prevalence of critical illness per 100,000 people of 19.4 (95% CI 16.4–23.0). The proportion of patients in hospital who were critically ill was 10.5% (95% CI 8.8–12.3%). Among the critically ill, 125 [95% CI 94.0% (88.4–97.0%)] were cared for in general wards.

Conclusions

The prevalence of critical illness was higher than previous, indirect estimates. One in ten hospitalized patients were critically ill, the large majority of which were cared for in general wards. This suggests a hidden burden of critical illness of potential public health, health system and hospital management significance.

Background

The surges of unwell patients during the COVID-19 pandemic have brought critical illness to public attention. Critical illness is a state of ill health with vital organ dysfunction, a high risk of imminent death if care is not provided and a potential for reversibility [1,2,3]. Critical illness is not confined to specific medical specialties or the patient’s underlying diagnosis. Its defining attributes are that the condition is severe and time-sensitive, attributes strongly associated with prognosis and prompting urgent care to improve the prognosis.

Very little is known about the burden of critical illness in terms of prevalence and incidence. Information about critical illness is rarely collected in administrative data and registries. Despite this information gap, hospitals make key resource decisions concerning critical illness, such as the number of beds in intensive care units (ICUs) and high dependency units (HDUs), staffing levels and clinical routines. Improved knowledge about the burden of critical illness would assist the tailoring of health services to patient needs and contribute to improved outcomes.

Previous attempts to estimate the burden of critical illness have important methodological limitations, notably ignoring critical illness among non-ICU populations [4, 5]. The number of ICU bed-days or ICU-admissions are poor proxies for the burden of critical illness, as they depend on available ICU-bed capacity [6]: per 100,000 people, the USA and Germany have 34 and 29 ICU beds, respectively, whereas the UK and Sweden have only seven and six, respectively [7]. It is unlikely that there is a fourfold difference in the burden of critical illness between these countries—more likely that health systems define or use ICU beds differently [5, 8,9,10,11]. Adhikari et al. estimated the global incidence of adult critical illness to be 30–45 million per year through extrapolation of the incidence of specific diagnoses in a North American ICU registry to the rest of the world [2]. This figure provides a useful starting point, but the method uses retrospective indirect data, omits all critical illness outside ICUs and due to other diagnoses, and does not account for health system diversity.

An alternative method to assess the burden is to examine all patients and use their physiological status to identify critical illness. The examination of patients’ vital signs is quick in clinical practice[12, 13] and useful for assessing illness severity. Patients with severely deranged vital signs have a high risk of short-term mortality, comparable to the population of patients admitted to ICU, and timely care has the potential to reverse their condition—i.e., they are critically ill [13,14,15,16,17,18,19].

The aim of this study was to estimate the prevalence of critical illness in a Swedish region using a point-prevalence examination of patients’ vital signs. Secondary aims were to estimate the proportion of hospital inpatients who are critically ill and to describe the in-hospital location of critically ill patients.

Methods

Design and study setting

We conducted a prospective, multi-center, population-based, point-prevalence study in the Sörmland region in the central-eastern part of Sweden. Sörmland has an adult (> 18 years ) population of 228,594 (2017) with a life-expectancy similar to the national level, but slightly lower socioeconomic status [20, 21]. Sweden has tax-funded universal health coverage that includes care in hospital, and privatized hospital care is negligible in Sörmland. The region has three district hospitals for in-patient care, with a total of 470 beds of which 12 were ICU beds at the time of the study, similar to national ICU bed capacity of 5.0 per 100,000 pre-COVID-19 [22]. For tertiary care, such as neuro- and thoracic surgery, patients are referred to university hospitals in adjacent regions. The study was part of the multi-center, multi-country Critical Illness Prevalence and Outcome Study (CRISPOS) and follows the STROBE guidelines for observational studies.

Study population

All adult (> 18 years) hospital in-patients from the Sörmland Region were examined on three specific days in 2017–2018. This included patients in the Sörmland hospitals plus those from Sörmland who had been referred and were receiving in-hospital care in university hospitals. Patients in all somatic-care wards and those in the emergency departments waiting for transfer to in-hospital care were included. The study included only patients admitted to hospitals, as we assumed the number of critically ill patients outside of hospitals was minimal. This assumption is based on the universal access in Sweden to high-quality, pre-hospital emergency services and hospital care, so that all people with acute, reversible, life-threatening illness are expected to be admitted to hospital. People with sudden out-of-hospital death and those receiving end-of-life care in their homes or in nursing homes do not have reversible disease.

We included patients with the decision, “do not resuscitate in the event of a cardiac arrest” (DNR). Most patients with DNR decisions in Swedish hospitals receive both supportive and curative care, such as oxygen, antibiotics, emergency surgery, and some are even treated in ICU [23]—with the goal of curing the acute condition and returning the patient to their usual state-of-health—therefore, they fulfil the definition of being critically ill. Moribund patients—as identified by the attending nurse—have a non-reversible condition and were excluded, as well as patients in the operating theatres, those in psychiatric wards or with a primarily psychiatric cause of admission, and women in active labor.

Data collection

Data collection took place on 3 days in 2017–2018 that were selected to account for seasonal variability and to be feasible for the study. Before each data collection, all data collectors received a 1-day standardized training in the study methods and all medical equipment for the examinations was quality assured by medical technicians. On each occasion, in-patients in all wards and units were examined by a team of nursing students under the supervision of experienced doctors and nurses. In the university hospitals, patients from Sörmland were identified by their personal number, and traced through postal codes, referral administrative data and ambulance transport records and the patients were examined by trained doctors and nurses. For patients who were not present in their beds when the team arrived, at least one attempt was made to locate the patient later in the same day, including in other wards and units, such as recovery rooms. Data on the main diagnosis by ICD-10 code, admitting specialty, previous surgery and DNR decisions were extracted from the medical records. Population data were retrieved from Statistics Sweden. Double data entry into a spreadsheet and discrepancy checking were performed before the study database was locked.

Definitions

We used the definition of critical illness as ‘a state of ill health with vital organ dysfunction, a high risk of imminent death if care is not provided and a potential for reversibility’ [3]. The presence of one or more severely deranged vital signs was used to classify critical illness, hereafter referred to as ‘danger signs’ (Fig. 1). This method does not rely on specific diagnoses or the location of care in the hospital and is pragmatic to use in clinical practice. We used a-priori decided cutoffs for danger signs adapted from the rapid response system in Karolinska University Hospital and previous research [17, 24, 25].

Fig. 1
figure 1

Included parameters and cutoffs for danger signs

Full size image

The in-hospital location where the patient was being treated was defined as’ICU’ (care in a unit specified as ICU by the hospital),’HDU’ (care in a post-operative recovery area or a bed with continuous monitoring of vital signs and availability of non-invasive ventilation) or ‘general ward’. The main diagnosis was defined by using the ICD-10 codes written in the medical records.

Data analysis

The prevalence of critical illness was calculated as all patients with one or more danger sign identified in the three data collections divided by the number of adults in the Sörmland region > 18 years in the December 31st, 2017, census [multiplied by three to match the three data collections (N = 228,594 × 3 = 685,782)]. For the secondary aim of estimating the proportion of hospital in-patients who were critically ill, the total number of examined patients was used as the denominator. Missing data for vital signs were classified as ‘not a danger sign’, using imputation to the most frequent value. Descriptive statistics were used for patient characteristics. In applicable analyses, confidence intervals of 95% were used. Stata /IC 15 (Stata Corp, College Station, TX) was used for the statistical analyses.

Sensitivity analyses

As there are no generally agreed criteria for critical illness, we performed some sensitivity analyses. First, due to the argument that a DNR decision could indicate non-reversible disease, we classified all patients with DNR decision as being ‘not critically ill’. Second, due to the possibility that intensive therapies could normalize physiological parameters, we classified all patients receiving care in ICU and HDU as critically ill—irrespective of their vital signs. Third, we calculated the patients’ National Early Warning Scores (NEWS)[15] and classified those with a score of ≥ 7 or alternatively ≥ 5 and as critically ill (Additional file 1: Fig. S1).

Ethical considerations

The study followed the principles of the Helsinki declaration and its subsequent revisions. Ethical permission was granted by the Swedish Ethical Review Board, Stockholm (EPN 2017/1907-31/1). Participants were included after receiving written information, and as the study was low-risk and the participation of patients with reduced conscious level was vital for the validity of the study, consent was assumed from such patients, in the absence of any objection from the patients or next-of-kin.

Results

A total of 1437 patients were screened in the hospitals, 1391 were eligible and 1269 patients were included in the study (Fig. 2). The patients’ median age was 74 years (IQR 62–83) and 640 (50%) were female. Of all patients, 820 (65%) were cared for in the departments of internal medicine, and the remainder in the surgical, and obstetrics and gynecology departments (Table 1). There was a large variety of diagnoses (n = 822), and 145 patients (11%) had one of the three most common diagnoses, (pneumonia, stroke, or heart failure). During their time in hospital before inclusion in the study, 208 (16%) of patients had undergone a surgical procedure and 233 (18%) had a DNR decision made for their care. There were 1209 (95%) patients who received care in general wards, 32 (3%) in HDUs and 29 (2%) in ICUs. Of all patients, 47 (4%) had been transferred for care in university hospitals.

Fig. 2
figure 2

Flow diagram of the inclusion process

Full size image
Table 1 Characteristics and care of the included patients by critical illness
Full size table

Critical illness was present in 133 patients. This gave an adult population prevalence of critical illness per 100,000 people of 19.4 (16.4–23.0). The proportion of patients in hospital who were critically ill was 10.5% (8.8–12.3) (Fig. 3). Among the critically ill, 125 [94.0 (88.4–97.0)] were cared for in general wards, 3 [2.3% (0.7–7.7)] in HDUs and 5 [3.8% (1.6–8.7)] in ICUs (Fig. 3).

Fig. 3
figure 3

Proportion of critically ill patients among all hospital in-patients and the in-hospital location of critically ill patients

Full size image

Out of the 133 critically ill patients, 74 [55.6% (47.2–64.1)] were male, 8 [6.0% (2.0–10.1)] had undergone surgery and 115 [86.5% (80.1–92.3)] were admitted to medical departments. Fifty-nine [44.3% (36.8–52.8)] had a DNR decision (Table 1). Out of the 10,526 vital signs assessments conducted, there were 25 missing values (0.2%).

In the sensitivity analyses, the prevalence of critical illness per 100,000 people was 10.8 (8.5–13.6) when excluding patients with a DNR decision; 27.1 (23.5–31.3) when also including all patients in ICUs and HDUs as critically ill; 11.1 (8.9–13.9) using NEWS ≥ 7 to classify critical illness and 28.4 (24.7–32.7) using NEWS ≥ 5 to classify critical illness. (Additional file 1: Tables S2 and S3).

Discussion

We have found a population prevalence of adult critical illness per 100,000 people of 19.4 in the Sörmland Region of Sweden. Of all hospitalized patients, 10.5% were critically ill and of these critically ill, 94% were cared for in general wards. To our knowledge, this is the first attempt to estimate the burden of critical illness at population level using prospective enrolment and examination of patients.

The prevalence of critical illness

This burden of critical illness is higher than previously considered, but comparisons with previous estimates are challenging. Since the burden of acute illnesses is often measured using the incidence, (the flow of cases into the system per time unit), a conversion of our prevalence estimate is needed. Using an assumption that critical illness on average has a duration of 1.1 days (based on the proxy of the median duration of patients’ care in Swedish ICUs) [26], our findings would correspond to 17.6 new cases of critical illness per 100,000 people per day. This number is substantially higher than the estimate by Adhikari et al. of 1.3–1.9 per 100,000 people per day in Europe [2]. The difference could be explained by our more liberal criteria for critical illness rather than limiting inclusion to patients with certain syndromes and those treated in ICUs. We do not believe that our criteria were too liberal. Severely abnormal vital signs have been shown to identify a group of patients with a high risk of death—indeed their risk of 26–28% 30-day mortality [13] is higher even than the risk for patients admitted to ICUs (17%) [19]. The burden of critical illness in our study is also larger than the burden of major acute diagnoses that receive substantial public, research, and policy interest. There are 0.5 strokes [27], 2.7 hip fractures [28], and 2.8 [29] myocardial infarctions in Sweden per 100,000 people per day, with 30-day mortalities of 11% [27], 8% [30], and 8% [31], respectively. Critical illness appears to be a larger health issue than is usually recognized.

There are important policy implications of this large burden of critical illness. The health system requirements need to be specified for these patients, as do the competencies of health workers to care for them, and the clinical systems and routines to prioritize [32, 33] for them to be quickly identified as critically ill and promptly treated—even when resources are limited such as at 3 am on a Sunday night [34]. Re-designing health services to have an increased focus on critical illness, in the same way as the longstanding focus on diagnoses with high mortalities [35,36,37], would target the highest risk patients and—as they are common—could be favorable for quality improvement, research, and innovation. Improving care of severely ill patients would benefit patients suffering from any underlying condition and have the advantage of improving care for patients lacking a definitive diagnosis, those with multimorbidity and those who have been mis-diagnosed.

The proportion of patients in hospital that are critically ill

Among all in-patients, 10.5% were critically ill, a finding in-line with previous research. Studies from university hospitals in Finland and Sweden report proportions of patients with single parameter signs of 8.4% and 12% and NEWS ≥ 7 of 6.0% and 6.5%, respectively [13, 38]. In our population-based study, most patients were treated in district hospitals, and of these patients, 10% were critically ill (versus 13% in the university hospitals). While it may be thought that most patients with critical illness would be referred to ICUs and to larger hospitals, the findings do not support that perception.

The in-hospital location of critically ill patients

A large majority of critically ill patients (94%) were cared for in general wards. There are, to our knowledge, no previous studies of the in-hospital location of critically ill patients, but the presence of very ill patients in general wards and not just in HDUs and ICUs has been described in work on sepsis, early warning systems (EWS), rapid response teams (RRT)—and from low-resource settings [16, 39,40,41,42,43,44]. While critical illness does not equal need of care in an ICU, the implications of this finding are debatable [45]. An argument could be proposed that an ICU-bed capacity corresponding to that in the USA and Germany (> 20 per 100,000 people) could be needed to care for these patients. However, from a health system perspective, it would be more rational to provide care at the lowest effective level to maximize the use of resources [46, 47]. ICUs may offer better care in some cases [48], but at a high cost [49]—and many countries in the world have less than one ICU bed per 100,000 people [50]. ICU care did not provide better outcomes for patients lacking a strong ICU indication and was not always cost-effective in the COVID-pandemic [47, 51]. For a large proportion of the critically ill, HDUs providing less advanced critical care together with optimized care of critical illness in general wards may be a better approach to prevent deterioration, the need for ICU care and death [47, 52]. This requires that health systems ensure adequate provision of fundamental critical care in the general wards, such as through training of staff, the use of EWS and RRT, and focused quality improvement [52,53,54]. A recent consensus has specified ‘Essential Emergency and Critical Care’, 40 clinical processes that are effective, lifesaving and feasible to deliver in general wards and all other parts of hospitals [34].

Strengths and limitations

The strengths of the study include the use of criteria to define critical illness that are neither dependent on the underlying diagnosis nor the hospital location; the prospective examination of all study participants by trained data collectors and quality secured equipment; the few missing data points; and the high inclusion rate of patients from all types of wards and hospitals in which a well-defined population are admitted for care, including referrals to high level care in other regions.

The study had limitations. First, the pragmatic vital signs-based criteria used may have missed some high-risk patients whose vital signs were insufficiently deranged or had been stabilized through care. Conversely, some patients with adapted physiology due to chronic disease may have been classified as being critically ill. Second, for logistical reasons, we could neither include patients in the operating theatres nor patients in the emergency units awaiting a decision about hospital admission, some of whom may have been critically ill. Third, data collections took place before the COVID-19 pandemic, in the daytime on weekdays and the prevalence may differ over time and between weekends and nights [55]. Fourth, the prevalence estimates changed when alternative criteria for critical illness were used in the sensitivity analyses, highlighting the challenge of identifying critical illness and the high importance of a process towards agreed criteria for critical illness [3]. Fifth, we assumed that there is no critical illness in the community. If this assumption is false, the burden of critical illness may be greater than our estimate. Finally, this study was from one region in one country. However, we have no reason to believe that there are substantial differences between settings similar to Sweden, and a cautious transfer of the findings could be useful for many health systems.

Conclusion

In this prospective, multi-center population-based study, we have found a prevalence of adult hospital-treated critical illness of 19.4 per 100,000 people. Among hospitalized patients, one in ten were critically ill, a large majority of which (94%) were cared for outside of ICUs and HDUs. Our estimates are higher than previous, indirect estimates, suggesting a hidden burden of critical illness of potential public health, health-system, and hospital management significance.

Availability of data and materials

Access to deidentified data may be provided to researchers after provision of a study protocol and justification to the corresponding author.

Abbreviations

DNR:

Do not resuscitate in the event of a cardiac arrest

EWS:

Early warning systems

HDU:

High dependency unit

ICD-10:

International Classification of Diseases, Tenth Revision

ICU:

Intensive care unit

NEWS:

National Early Warning Score

RRT:

Rapid response team

References

  1. Kelley MA. Critical care medicine–a new specialty? N Engl J Med. 1988;318(24):1613–7.

    Article  CAS  PubMed  Google Scholar 

  2. Adhikari NK, Fowler RA, Bhagwanjee S, Rubenfeld GD. Critical care and the global burden of critical illness in adults. Lancet. 2010;376(9749):1339–46.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kayambankadzanja RK, Schell CO, Wärnberg MG, Tamras T, Mollazadegan H, Holmberg M, Alvesson HM, Baker T. Towards definitions of critical illness and critical care using concept analysis. BMJ Open. 2022;12(9): e060972.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wunsch H, Rowan KM, Angus DC. International comparisons in critical care: a necessity and challenge. Curr Opin Crit Care. 2007;13(6):725–31.

    Article  PubMed  Google Scholar 

  5. Murthy S, Wunsch H. Clinical review: International comparisons in critical care - lessons learned. Crit Care. 2012;16(2):218.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wunsch H, Angus DC, Harrison DA, Collange O, Fowler R, Hoste EA, de Keizer NF, Kersten A, Linde-Zwirble WT, Sandiumenge A. Variation in critical care services across North America and Western Europe. Crit Care Med. 2008;36(10):2787-e2788.

    Article  PubMed  Google Scholar 

  7. Ma X, Vervoort D. Critical care capacity during the COVID-19 pandemic: global availability of intensive care beds. J Crit Care. 2020;58:96–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rhodes A, Ferdinande P, Flaatten H, Guidet B, Metnitz PG, Moreno RP. The variability of critical care bed numbers in Europe. Intensive Care Med. 2012;38(10):1647–53.

    Article  CAS  PubMed  Google Scholar 

  9. Marshall JC, Bosco L, Adhikari NK, Connolly B, Diaz JV, Dorman T, Fowler RA, Meyfroidt G, Nakagawa S, Pelosi P, et al. What is an intensive care unit? A report of the task force of the World Federation of Societies of Intensive and Critical Care Medicine. J Crit Care. 2016;37:270–6.

    Article  PubMed  Google Scholar 

  10. Wallace DJ, Angus DC, Seymour CW, Barnato AE, Kahn JM. Critical care bed growth in the United States. A comparison of regional and national trends. Am J Respir Crit Care Med. 2015;191(4):410–6.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Phua J, Faruq MO, Kulkarni AP, Redjeki IS, Detleuxay K, Mendsaikhan N, Sann KK, Shrestha BR, Hashmi M, Palo JEM, et al. Critical care bed capacity in Asian Countries and Regions. Crit Care Med. 2020;48(5):654–62.

    Article  PubMed  Google Scholar 

  12. Kassebaum NJ, Bertozzi-Villa A, Coggeshall MS, Shackelford KA, Steiner C, Heuton KR, Gonzalez-Medina D, Barber R, Huynh C, Dicker D, et al. Global, regional, and national levels and causes of maternal mortality during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet. 2014;384(9947):980–1004.

    Article  Google Scholar 

  13. Friman O, Bell M, Djarv T, Hvarfner A, Jaderling G. National early warning score vs rapid response team criteria-prevalence, misclassification, and outcome. Acta Anaesthesiol Scand. 2019;63(2):215–21.

    Article  PubMed  Google Scholar 

  14. Bleyer AJ, Vidya S, Russell GB, Jones CM, Sujata L, Daeihagh P, Hire D. Longitudinal analysis of one million vital signs in patients in an academic medical center. Resuscitation. 2011;82(11):1387–92.

    Article  PubMed  Google Scholar 

  15. Smith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation. 2013;84(4):465–70.

    Article  PubMed  Google Scholar 

  16. Kruisselbrink R, Kwizera A, Crowther M, Fox-Robichaud A, O’Shea T, Nakibuuka J, Ssinabulya I, Nalyazi J, Bonner A, Devji T, et al. Modified Early Warning Score (MEWS) identifies critical illness among ward patients in a resource restricted setting in Kampala, Uganda: a prospective observational study. PLoS ONE. 2016;11(3): e0151408.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Baker T, Blixt J, Lugazia E, Schell CO, Mulungu M, Milton A, Castegren M, Eriksen J, Konrad D. Single deranged physiologic parameters are associated with mortality in a low-income country. Crit Care Med. 2015;43(10):2171–9.

    Article  PubMed  Google Scholar 

  18. Konrad D, Jaderling G, Bell M, Granath F, Ekbom A, Martling CR. Reducing in-hospital cardiac arrests and hospital mortality by introducing a medical emergency team. Intensive Care Med. 2010;36(1):100–6.

    Article  PubMed  Google Scholar 

  19. Rydenfelt K, Engerström L, Walther S, Sjöberg F, Strömberg U, Samuelsson C. In-hospital vs 30-day mortality in the critically ill - a 2-year Swedish intensive care cohort analysis. Acta Anaesthesiol Scand. 2015;59(7):846–58.

    Article  CAS  PubMed  Google Scholar 

  20. Statistikmyndigheten.Registerbaserad folk- och bostadsräkning, Census. 2023. https://www.scb.se/hitta-statistik/statistik-efter-amne/befolkning/befolkningens-storlek-och-forandringar/registerbaserad-folk--och-bostadsrakning-census/. Accessed 3 Feb 2023.

  21. Region Sörmland. Hälsan i Södermanland version 2020–06. 2020. https://samverkan.regionsormland.se/siteassets/utveckling-och-samarbete/halsan-i-sodermanland---varen-2020.pdf. Accessed 3 Feb 2023.

  22. Berger E, Winkelmann J, Eckhardt H, Nimptsch U, Panteli D, Reichebner C, Rombey T, Busse R. A country-level analysis comparing hospital capacity and utilisation during the first COVID-19 wave across Europe. Health Policy. 2022;126(5):373–81.

    Article  PubMed  Google Scholar 

  23. Svenska Intensivvårdsregistret. Svenska Intensivvårdsregistrets Årsrapport för 2021. 2022. https://www.icuregswe.org/globalassets/arsrapporter/arsrapport_2021.pdf. Accessed 30 Jan 2023.

  24. Bell MB, Konrad D, Granath F, Ekbom A, Martling CR. Prevalence and sensitivity of MET-criteria in a Scandinavian University Hospital. Resuscitation. 2006;70(1):66–73.

    Article  PubMed  Google Scholar 

  25. Parker RK, Mwachiro EB, Mwachiro MM, Pletcher J, Parker AS, Many HR. Mortality Prediction in Rural Kenya: A Cohort Study of Mechanical Ventilation in Critically Ill Patients. Crit Care Explor. 2019;1(12): e0067.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Svenska Intensivvårdsregistret. Svenska intensivvårdsregistrets årsrapport 2019. 2020. https://www.icuregswe.org/globalassets/arsrapporter/arsrapport_2019_final.pdf. Accessed 30 Jan 2023.

  27. Aked J, Delavaran H, Norrving B, Lindgren A. Temporal trends of stroke epidemiology in southern Sweden: a population-based study on stroke incidence and early case-fatality. Neuroepidemiology. 2018;50(3–4):174–82.

    Article  PubMed  Google Scholar 

  28. Rosengren B, Björk J, Cooper C, Abrahamsen B. Recent hip fracture trends in Sweden and Denmark with age-period-cohort effects. Osteoporos Int. 2017;28(1):139–49.

    Article  CAS  PubMed  Google Scholar 

  29. Socialstyrelsen. Statistikdatabas för diagnoser. 2023. https://sdb.socialstyrelsen.se/if_par/resultat.aspx. Accessed 3 Feb 2023.

  30. Mattisson L, Bojan A, Enocson A. Epidemiology, treatment and mortality of trochanteric and subtrochanteric hip fractures: data from the Swedish fracture register. BMC Musculoskelet Disord. 2018;19(1):369.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Chung S-C, Gedeborg R, Nicholas O, James S, Jeppsson A, Wolfe C, Heuschmann P, Wallentin L, Deanfield J, Timmis A. Acute myocardial infarction: a comparison of short-term survival in national outcome registries in Sweden and the UK. Lancet. 2014;383(9925):1305–12.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Allen D, Harkins KJ. Too much guidance? The Lancet. 2005;365(9473):1768.

    Article  CAS  Google Scholar 

  33. Maaløe N, Ørtved AMR, Sørensen JB, Sequeira Dmello B, van den Akker T, Kujabi ML, Kidanto H, Meguid T, Bygbjerg IC, van Roosmalen J, et al. The injustice of unfit clinical practice guidelines in low-resource realities. Lancet Glob Health. 2021;9(6):e875–9.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Schell CO, Khalid K, Wharton-Smith A, Oliwa J, Sawe HR, Roy N, Sanga A, Marshall JC, Rylance J, Hanson C, et al. Essential emergency and critical care: a consensus among global clinical experts. BMJ Glob Health. 2021;6(9): e006585.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Richards CT. Strengthening the stroke chain of survival in community emergency departments. J Am College Emerg Phys Open. 2022;3(4): e12763.

    Article  Google Scholar 

  36. Wang Z, Chen M, Luo X, Xiong X, Ma F. Effectiveness of optimised care chain for hip fractures in elderly Chinese. Int J Health Plann Manage. 2021;36(5):1445–64.

    Article  PubMed  Google Scholar 

  37. Szummer K, Wallentin L, Lindhagen L, Alfredsson J, Erlinge D, Held C, James S, Kellerth T, Lindahl B, Ravn-Fischer A. Improved outcomes in patients with ST-elevation myocardial infarction during the last 20 years are related to implementation of evidence-based treatments: experiences from the SWEDEHEART registry 1995–2014. Eur Heart J. 2017;38(41):3056–65.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Tirkkonen J, Olkkola KT, Huhtala H, Tenhunen J, Hoppu S. Medical emergency team activation: performance of conventional dichotomised criteria versus national early warning score. Acta Anaesthesiol Scand. 2014;58(4):411–9.

    Article  PubMed  Google Scholar 

  39. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, Colombara DV, Ikuta KS, Kissoon N, Finfer S, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200–11.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Seymour CW, Liu VX, Iwashyna TJ, Brunkhorst FM, Rea TD, Scherag A, Rubenfeld G, Kahn JM, Shankar-Hari M, Singer M, et al. Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):762–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Prytherch DR, Smith GB, Schmidt PE, Featherstone PI. ViEWS–towards a national early warning score for detecting adult inpatient deterioration. Resuscitation. 2010;81(8):932–7.

    Article  PubMed  Google Scholar 

  42. Maharaj R, Raffaele I, Wendon J. Rapid response systems: a systematic review and meta-analysis. Crit Care. 2015;19:254.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Vincent J-L. Critical care - where have we been and where are we going? Crit Care. 2013;17(1):S2.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Kayambankadzanja RK, Schell CO, Mbingwani I, Mndolo SK, Castegren M, Baker T. Unmet need of essential treatments for critical illness in Malawi. PLoS ONE. 2021;16(9): e0256361.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Verdonk F, Zacharowski K, Ahmed A, Orliaguet G, Pottecher J. A multifaceted approach to intensive care unit capacity. Lancet Public Health. 2021;6(7): e448.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Valley TS, Noritomi DT. ICU beds: less is more? Yes. Intensive Care Med. 2020;46(8):1594–6.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Chang DW, Shapiro MF. Association between intensive care unit utilization during hospitalization and costs, use of invasive procedures, and mortality. JAMA Intern Med. 2016;176(10):1492–9.

    Article  PubMed  Google Scholar 

  48. Phua J, Hashmi M, Haniffa R. ICU beds: less is more? Not sure Intensive Care Med. 2020;46(8):1600–2.

    Article  PubMed  Google Scholar 

  49. Gooch RA, Kahn JM. ICU bed supply, utilization, and health care spending: an example of demand elasticity. JAMA. 2014;311(6):567–8.

    Article  CAS  PubMed  Google Scholar 

  50. Ayebale ET, Roche AM, Kassebaum NJ, Biccard BM. Africa’s critical care capacity before COVID-19. S Afr J Anaesthesia Analgesia. 2020;26(3):162–4.

    Article  Google Scholar 

  51. Cleary SM, Wilkinson T, Tamandjou Tchuem CR, Docrat S, Solanki GC. Cost-effectiveness of intensive care for hospitalized COVID-19 patients: experience from South Africa. BMC Health Serv Res. 2021;21(1):82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Schell CO, Gerdin Warnberg M, Hvarfner A, Hoog A, Baker U, Castegren M, Baker T. The global need for essential emergency and critical care. Crit Care. 2018;22(1):284.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Hogan H, Carver C, Zipfel R, Hutchings A, Welch J, Harrison D, Black N. Effectiveness of ways to improve detection and rescue of deteriorating patients. Br J Hosp Med. 2017;78(3):150–9.

    Article  Google Scholar 

  54. Schmidt PE, Meredith P, Prytherch DR, Watson D, Watson V, Killen RM, Greengross P, Mohammed MA, Smith GB. Impact of introducing an electronic physiological surveillance system on hospital mortality. BMJ Qual Saf. 2015;24(2):176–7.

    Article  PubMed  Google Scholar 

  55. Indik JH. Is it like night and day, or weekend?∗. J Am Coll Cardiol. 2018;71(4):412–3.

    Article  PubMed  Google Scholar 

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Acknowledgements

Nursing students from Mälardalen University (MDU) collected the data. Carina Palesjö, Jenny Olofsson and Ida Åhlin and colleagues coordinated and supervised nursing student data collectors from MDU. Sandra Prevedan, Christel Andersson, Eva Karlsten, Inger Jönsson, Tua Steiber, Linnea Fransson, Rebecka Grundin, Alexandra Vahlund, Therese Alpe, Boel Pettersson, Mina Voghera, Anna Dahlberg and colleagues supervised the data collections. Sara Ulin, Joanna Wessbergh, Elin Söderman, Patric Bränderson and Tomas Pettersson and colleagues enabled the data collections in University Hospitals and collected data.Lina Lindfors. Henrik Roos, Rafat Ghanni, Armand Viberg and colleagues quality assured the medical equipment. Johan Printz and Josefine Back assisted the data collection from medical records. Boel Pettersson, Frida Schell, Simon-Fredrik Schell and Camilla Veliz-Munoz conducted the double data entry. Gaetano Marrone advised the statistical analysis. Kristian Waern-Bugge, Martin Serrander, Martina Olsson, Harald Zetterquist, Pernilla Larsson and Petri Olivius provided logistical support.

Funding

This research was supported by Grant DLL-652801 from the Centre for Clinical Research Sörmland, Uppsala University, Sweden (COS) and RFR-931271 from Regional Research Council Mid Sweden (COS, AW). Open access funding was provided by Karolinska Institute. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden

    Carl Otto Schell, Andreas Wellhagen, Cecilia Stålsby Lundborg & Tim Baker

  2. Centre for Clinical Research Sörmland, Uppsala University, Eskilstuna, Sweden

    Carl Otto Schell, Andreas Wellhagen, Petronella Bjurling-Sjöberg & Markus Castegren

  3. Department of Medicine, Nyköping Hospital, Sörmland Region, Nyköping, Sweden

    Carl Otto Schell

  4. Department of Anaesthesia and Intensive Care, Nyköping Hospital, Sörmland Region, Nyköping, Sweden

    Andreas Wellhagen

  5. Department of Surgical Sciences, Anaesthesiology and Intensive Care, Uppsala University, Uppsala, Sweden

    Miklós Lipcsey

  6. Department of Surgical Sciences, Hedenstierna Laboratory, Uppsala University, Uppsala, Sweden

    Miklós Lipcsey

  7. School of Medical Sciences, Örebro University, Örebro, Sweden

    Lisa Kurland

  8. Department of Emergency Medicine, Örebro University, Örebro University Hospital, Örebro, Sweden

    Lisa Kurland

  9. Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden

    Petronella Bjurling-Sjöberg

  10. Department of Patient Safety, Region Sörmland, Eskilstuna, Sweden

    Petronella Bjurling-Sjöberg

  11. Perioperative Medicine and Intensive Care (PMI), Karolinska University Hospital, Stockholm, Sweden

    Markus Castegren

  12. Department of Physiology and Pharmacology (FyFa), Karolinska Institutet, Stockholm, Sweden

    Markus Castegren

  13. Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK

    Tim Baker

  14. Department of Emergency Medicine, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania

    Tim Baker

Authors
  1. Carl Otto Schell
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  2. Andreas Wellhagen
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  8. Tim Baker
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Contributions

COS, MC and TB conceptualized and designed the study. COS, AW, ML, LK, PBS and MC acquired the data. COS and TB drafted the manuscript. CSL, MC and TB provided supervision. All authors obtained funding, analyzed and interpretated the data, critically revised the manuscript for important intellectual content, and approved the final manuscript.

Corresponding author

Correspondence to Carl Otto Schell.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Swedish Ethical Review Board, Stockholm (EPN 2017/1907-31/1). Participants were included after receiving written information, and as the study was low-risk and the participation of patients with reduced conscious level was vital for the validity of the study, consent was assumed from such patients, in absence of any objection from the patients or next-of-kin.

Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1:

Figure S1. Parameters for National Early Warning score (NEWS).Table S2. Sensitivity analyses. Prevalence of adult hospital-treated critical illness and the proportion of critically ill patients in hospital.Table S3. Sensitivity analyses. In-hospital location.

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Schell, C.O., Wellhagen, A., Lipcsey, M. et al. The burden of critical illness among adults in a Swedish region—a population-based point-prevalence study. Eur J Med Res 28, 322 (2023). https://doi.org/10.1186/s40001-023-01279-0

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European Journal of Medical Research

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