The transition of care from the ICU to hospital ward is a challenging period in the care continuum for patients who are recovering from critical illness. These transitions involve not only moving from high-intensity to lower-intensity care settings but also involve communication of large quantities of complex and important information among multiple healthcare providers (1). Frequently the result is a breakdown in communication that may lead to medical errors, overuse of diagnostic tests (2), patient dissatisfaction (3–5), and increased cost (6). The transition from ICU to hospital ward, including the associated risks of ICU readmission and death after discharge from the ICU have been extensively documented (7–13), but less is known about safety of care during and after transition from ICU to hospital ward. It has been suggested that patients may be at increased risk of adverse events (AEs) after ICU discharge, but no studies have directly examined this aspect of the transition from ICU to hospital ward (14–16).
Critically ill patients are at increased risk of AEs, a negative event leading to patient harm and caused by management rather than the underlying condition of the patient (17), with an estimated one in four ICU patients experiencing an AE at some point during their hospital admission (18,19). Consequences of these AEs include prolonged ICU and hospital stay, ICU readmission, and increased risk of hospital mortality (18,20); it is estimated that 6.3% of all patients who are discharged from ICU will be readmitted during their hospital stay and 6.8% of all patient discharged from the ICU will die in hospital (21). Half of AEs and 11% of unplanned ICU readmissions are potentially preventable (22–24). Collectively, this evidence suggests that there may be an important opportunity to improve the safety of care for critically ill patients during the transition from ICU to hospital ward. However, an understanding of AEs in critically ill patients during this period in their care is needed to inform interventions to improve the safety of the transition from ICU to hospital ward.
MATERIALS AND METHODS
We took advantage of an existing prospective cohort study on transitions from ICU to hospital wards to describe AEs in patients after discharge from ICU and to determine whether ICU and ward physicians could identify those patients at risk of AEs.
This retrospective chart review was nested within a larger prospective cohort study that examined transitions of care between ICU and hospital ward. Enrollment occurred between July 2014 and January 2016. Details of the prospective cohort study are reported elsewhere (1).
This study included patients admitted to 10 Canadian ICUs (St. Paul’s Hospital, Vancouver; University of Alberta Hospital, Edmonton; Foothills Medical Centre, Calgary; Rockyview General Hospital, Calgary; Peter Lougheed Centre, Calgary; South Calgary Health Campus, Calgary; Queensway Carleton Hospital, Ottawa; Sunnybrook Health Sciences Centre, Toronto; CHU de Québec-Université Laval (Hôpital de l’Enfant-Jésus), Québec City; and Centre Hôpitalier Universitaire de Sherbrooke, Sherbrooke).
Patients were included if they were 1) greater than or equal to 18 years old; 2) admitted to an adult, medical-surgical ICU for greater than or equal to 24 hours during the study period; 3) identified as ready for transfer to a hospital ward within the same hospital (any hospital bed not in the ICU); and 4) able to provide informed consent or have a surrogate willing to provide consent on their behalf. Patients were excluded if they did not speak English or French (researchers and research materials were only available in the two official Canadian languages) if they were admitted to a specialty ICU or transferred from the ICU to an outside facility. Consecutive patients from each site were enrolled until the target number of patients per site (n = 50) were enrolled (1,25).
Data Sources and Variables
Standardized data abstraction forms were used to collect AE data. An AE was defined using the Institute of Medicine definition—a negative event leading to patient harm and caused by management rather than the underlying condition of the patient (17). Key components of the definition were operationalized to ensure reliable classification by the evaluators: “negative” was defined as undesirable, “patient harm” was defined as a negative impact on patient health, and “causation” was defined as the event being a result of the healthcare process (commission or omission) rather than the patients’ own actions or disease progression. A two-stage review process previously described and adopted from other work on AE measurement was used to identify AEs (26,27). Two independent physicians fluent in English and/or French, blind to the others’ evaluation, reviewed de-identified photocopies of each patients’ physician progress, and consultation notes for the study period to identify AEs. Disagreement between reviewers was resolved by a third reviewer. Notes were collected for up to 7 consecutive calendar days after transfer to the accepting hospital ward.
The occurrence of an AE was coded dichotomously (yes or no) and was reported as the proportion of patients who had an AE and rates of events per patient-days. AEs were further classified according to the following characteristics: preventability, severity, and nature using the same two-stage approach (Supplemental Digital Content 1, http://links.lww.com/CCM/F388). Preventability was assessed on a 6-point scale adapted from previous studies of hospital AEs (virtually no evidence of preventability, slight to modest evidence of preventability, preventability not quite likely-less than 50/50 but close call, preventability more than likely-more than 50/50 but close call, strong evidence of preventability, and virtually certain evidence of preventability) (26). Severity was classified into six categories (laboratory abnormality requiring only change in therapy, up to 1 d of symptoms, more than 1 d of symptoms, nonpermanent disability, permanent disability, and death). The nature of the AEs was classified as follows: operative, medical-procedure related, drug-related, supportive care failure, diagnostic error, anesthesia-related, or other.
Secondary Outcomes (ICU Readmission and Hospital Mortality).
ICU readmission and hospital mortality were captured using standardized case report forms for each patient (1).
Patient and Hospital Characteristics.
Patient demographic characteristics were captured using standardized case report forms (1), and hospital characteristics were captured using a survey distributed to the participating hospitals.
The perceived quality of the transition from ICU to hospital ward was evaluated using paper surveys administered in person to the most responsible physician discharging the patient from the ICU and most responsible accepting ward physician at the time of transfer (1). The overall quality of the transition was rated using a 5-point Likert scale anchored by “the transfer went exceptionally well” and “the transfer was unacceptable.” The quality of the communication between physicians was rated using a 5-point Likert scale anchored by “Excellent” and “Poor.” In addition, ICU and ward physicians were asked to predict each patient’s risk of experiencing an AE, ICU readmission, and death in hospital using a 5-point scale ranging from “very low risk” to “very high risk.”
Descriptive statistics (frequencies, percentages, and medians with interquartile ranges) were used to summarize patient and institutional demographic variables and outcome variables (AE, ICU readmission, and hospital mortality). Chi-square and Wilcoxon rank-sum tests were used to compare demographic characteristics between those who experienced an AE and those who did not. Statistical significance was set at α equals to 0.05.
Receiver operating characteristic curves were used to visually examine the predictability of AEs, ICU readmission, and hospital mortality by ICU physicians before ICU discharge and ward physicians immediately after ICU discharge (predictable = very high/somewhat high and not predictable = neither high nor low/somewhat low/very low). The area under the curve (AUC) was used to quantify the discriminative ability of ICU physicians, ward physicians, and patient characteristics. Sensitivity and specificity for each model were calculated based on a cutoff value minimizing the difference between sensitivity and specificity.
Multivariable logistic regression was used to evaluate associations between AEs and patient and institutional characteristics. Variables with p value of less than 0.2 in the univariate analyses were entered into the model and sequentially eliminated until all variables had p value of less than 0.1.
This study was approved by the Conjoint Health Research Ethics Board at the University of Calgary, the coordinating center (REB13-0021) and at each study site. All participants provided written informed consent to participate in the study.
During the study period, there was an ICU-to-ward transfer requested for 1,495 patients from the 10 ICUs, 774 patients met initial criteria for the study, 621 patients consented to participate, and 451 patients were eventually transferred to a hospital unit and included in the study (1). The majority of patients were excluded because a researcher was not available at the time of transfer, the researcher was unable to contact the patients’ surrogate, the ICU length of stay was less than 24 hours, or the patient declined to participate (1). Patients who experienced an AE more commonly had chronic heart or vascular disease, chronic kidney disease, higher Charlson Comorbidity Index, higher Acute Physiology and Chronic Health Evaluation (APACHE) II score on ICU admission and discharge, and a higher Sequential Organ Failure Assessment score on ICU discharge than those who did not experience an AE (Supplemental Digital Content 2, http://links.lww.com/CCM/F389). AEs were more common in ICUs that had a greater bed occupancy on the day of discharge (Supplemental Digital Content 2, http://links.lww.com/CCM/F389).
Of 451 patients discharged from the ICU, 84 (18.6%) experienced at least one AE within 7 days of discharge from ICU. A total of 98 AEs were identified representing 38.0 AEs per 1,000 patient-days (censored at 7 d after discharge from ICU; Table 1). Thirty-six percent of AEs were judged to be potentially preventable (reviewer agreement = 26%). The majority of AEs resulted only in symptoms with fewer resulting in disability or death (Table 1). The most common AEs were supportive care failures (e.g., falls, pressure ulcers, fluid and electrolyte disorders) and drug-related. Most AEs occurred within 3 days of the transition from ICU to hospital ward (Fig. 1).
Using multivariable analysis, Charlson comorbidity score, APACHE II score on discharge from ICU, and bed occupancy on day of discharge were independently associated with increased odds of an AE (Table 2). Charlson comorbidity score and APACHE II score at discharge from ICU were also associated with increased odds of a preventable AE (Table 2). The model using each of these variables as predictor variables had a sensitivity of 0.65 and specificity of 0.64 with an AUC of 0.68 for predicting AEs (Fig. 2; and Supplemental Digital Content 3, http://links.lww.com/CCM/F390).
ICU Readmission and Hospital Mortality
Among those who had an AE after transfer from ICU to hospital ward, 26 patients (16.7%; 95% CI, 13.3–20.4%) were readmitted to the ICU and 21 patients died (13.4%; 95% CI, 10.3–16.8%) prior to hospital discharge. Most ICU readmissions occurred within 6 days of transfer and most deaths occurred 2 to 3 days after transfer (Fig. 1).
Physician Predictions of AEs, ICU Readmission, and Hospital Mortality
The sensitivity, specificity, and AUC for ICU and ward physician predictions of AEs, ICU readmission, and hospital death after transfer from the ICU were overall low (Fig. 2; and Supplemental Digital Content 3, http://links.lww.com/CCM/F390). Although sensitivity was low for all three outcomes, specificity was highest for hospital mortality. Predictions were most accurate for ICU readmission; the AUCs for ICU physicians and ward physicians were respectively 0.69 (95% CI, 0.57–0.82) and 0.77 (95% CI, 0.66–0.88).
Associations Between AEs and Clinical Outcomes
Using multivariable analysis and controlling for potential confounders (sex, age, Charlson Comorbidity Index, and APACHE II score at admission), patients who experienced an AE were more likely to be readmitted to the ICU (odds ratio [OR], 5.5; 95% CI, 2.4–13.0; p < 0.001), have a longer hospital stay (mean difference, 16.1 d; 95% CI, 8.4–23.7; p < 0.001) and die in hospital (OR, 4.6; 95% CI, 1.8–11.8; p = 0.001) than those who did not experience an AE.
We found that AEs are common after the transition from ICU to hospital ward—18% of ICU discharges experienced an AE within 7 days (most within 3 d) of transfer from ICU to hospital ward and 6% of AEs resulted in permanent disability or death. More than one-third of these AEs were considered preventable. AEs were not predictable by either the ICU or ward physician. Having two or more comorbidities, a higher APACHE II score at discharge and being in an ICU with a greater bed occupancy on the day of discharge were associated with increased odds of experiencing an AE. Those who had an AE stayed 16 days longer in hospital than those who did not and had 5.5 times greater odds of being readmitted to the ICU and 4.6 times greater odds of dying in hospital.
There is a paucity of evidence about AEs during the transition period from ICU to ward, despite wide recognition that this is a complex period in the care continuum that can compromise the quality and safety of care of critically ill patients (1,3–6,14,15). Studies have examined the quality of intrahospital transitions (28). For example, during the transition from emergency department to hospital ward (including the ICU) it has been found that 19% of patients experienced a potential safety event, 29% of emergency department physicians believed their patient experienced an AE or near miss and that the risk of a safety event is related to poor communication (29–31). Similarly, studies examining the transition of surgical patients (preoperative and postoperative) have found deficiencies in communication leading to increased risk of safety events and consequently, the development of standardized transition tools for surgical patients to improve the quality of transitions (28,32). Many of the existing studies of intrahospital transitions focus on critically ill patients because they are at a higher risk of experiencing AEs than the general hospital population; AEs occur in 9% of hospital patients compared with 15–51% of critically ill patients during their hospital stay (18,19,22,23,26,27,33). These studies do not identify the proportion of AEs associated with the transition from the ICU to hospital ward (18,20,22,27,34,35). Patients who are discharged from the ICU experience other outcomes that may be indicative of a safety event, such as ICU readmission (6%) and mortality before hospital discharge (7%) (21). Although we found the proportion of patients who were readmitted to ICU (4%) and who died before hospital discharge (1%) were lower than previous reports (likely, at least partially explained by censoring our analysis to 7 d after ICU transfer), we did find those who experienced an AE were more likely to be readmitted to the ICU and die while in hospital and 6% experienced an AE that resulted in permanent disability or death. Some of these negative outcomes can be prevented (22–24). We found that nearly one-third of AEs were considered by physicians to have a greater than 50% chance of being preventable, which is in keeping with existing evidence around preventability of AEs in critically ill patients (36,37). It is important to note that the agreement on preventability of the AEs by the two independent reviewers was low. This is a well-documented and persistent challenge in the patient safety literature (38–44) and an area of patient safety measurement that urgently needs innovative approaches.
Our study adds to our understanding of the safety of care after the transition from ICU to hospital ward by highlighting that it is difficult for physicians to predict AEs during the transition period from ICU to hospital ward. However, we identified some factors that are associated with AEs during this period in care (Charlson Comorbidity Index, APACHE II score on discharge, sex, and ICU bed capacity on day of discharge), which should give healthcare providers pause when transitioning patients from the ICU to hospital ward. Interestingly, males were more likely to experience an AE while females were more likely to experience a preventable AE. The association between AEs and sex has been previously reported in critically ill patients and thought to modify the relationship between AEs and other patient variables such as diagnosis and comorbidities, although the mechanism remains unclear (18). Patient acuity, comorbidities, and age have been found to be associated with AEs, in our study and by others (18, 20, 22, 33). As such, these variables are the foundation for at least eight risk prediction tools (45–48). Risk prediction tools may help to identify patients at risk for an AE, but the evidence of their clinical efficacy is lacking and should be further examined (49). One of the challenges of using patient characteristics to identify those at risk of experiencing an AE is that they are frequently static measures and nonmodifiable (e.g., comorbidities). Risk prediction tools that include dynamic and modifiable risk factors (e.g., number and severity of active medical problems) may be more helpful to clinicians as they could inform targeted interventions and timing of discharge—this should be the focus of future research.
Evidence suggests that AE rates are not only associated with patient characteristics but also associated with organizational factors (51). For example, hospital safety culture may be a better predictor of the severity of AEs than patient acuity (51). Similarly, allocation of healthcare resources (52, 53) and implementation of standardized best practices may influence the quality of transitions from ICU to ward (54). We found that higher bed occupancy on the day of discharge from ICU was associated with experiencing an AE, which is a novel finding that highlights a potential relationship between organizational factors and safety after the transition from ICU to ward. The relationship between safety during transition period from ICU to ward and bed occupancy should be further evaluated taking into account potential confounding factors such as bed occupancy in the hospital and hospital strain. Similarly, staffing (patient ratio, experience, discipline, profession, etc.) has been shown to be associated with the safety of care for critically ill patients (19, 55, 56). These factors should also be considered in future studies examining the safety of the transition from ICU to hospital ward.
Few studies have explored temporality of AEs and other safety-related incidents such as ICU readmission and hospital mortality during critically ill patients’ hospital stay (18, 46). We present new data to suggest that the first 3 days after ICU discharge are a particularly challenging period for patient safety (the majority of AEs occurred during this time), which is consistent with the findings of a single-center study of 167 patients that reported 10% of ICU discharges were associated with an AE within 72 hours of discharge from ICU (57). Given that the first 3 days after transition from ICU to hospital ward appears to be a high-risk period for critically ill patients, extending provisions to care for critically ill patients within the first 72 hours post-ICU discharge may be warranted. Post-ICU discharge follow-up programs may present an opportunity to improve the safety of this transition period as they have been found to be variably associated with decreased ICU readmission and hospital mortality (58, 59). The effect of post-ICU discharge follow-up programs on the rate of AEs is unknown but may represent an opportunity to improve the safety of transition from ICU to hospital ward. Similarly, a longer length of ICU stay for those at risk for AEs and step-down wards are other interventions that should be explored.
Although our study presents a rigorous evaluation of the safety of the transition from ICU to ward for a national, multicenter sample, there are some considerations when interpreting our findings. First, the evaluation of AEs, their severity, preventability, and predictability can be subjective. To overcome this limitation, we employed established methods for detecting AEs that included data abstraction by physicians who reviewed patient charts in duplicate and any disagreements were resolved through a third reviewer (26,27). Second, most hospitals were tertiary care teaching hospitals, and our results may not be generalizable to nonteaching community hospitals where the same clinicians may care for patients both in the ICU and on the ward. Similarly, we are unable to determine if excluded patients (no researcher available, unable to contact surrogate, ICU stay < 24 hr, declined to participate, did not speak English or French, transferred to another ICU) differed from included patients and if our findings are generalizable to ICU patient groups that were excluded. Third, although physician reviewers were asked to judge whether the AEs documented in the medical record were related to the ICU to hospital ward transition, they reported that the nature and quality of documentation in the medical record precluded such judgments (13). As such, we report that AEs are common after patient discharge from ICU, but it is unclear to what extent they are related to the transition of care or ICU care.
AEs are common among critically ill patients during the transition from ICU to ward and the first 3 days after ICU discharge are a particularly vulnerable time for critically ill patients. These AEs are associated with longer hospital stays and increased risk of ICU readmission and death in hospital. Neither ICU nor ward physicians are able to reliably predict which patients will experience an AE after discharge from ICU to hospital ward. Nevertheless, many AEs among critically ill patients are preventable, highlighting an important opportunity to improve the safety of care during the transition of care from the ICU to hospital ward.
1. Stelfox HT, Leigh JP, Dodek PM, et al. A multi-center prospective cohort study of patient transfers from the intensive care unit to the hospital ward. Intensive Care Med 2017; 43:1485–1494
2. Niès J, Colombet I, Zapletal E, et al. Effects of automated alerts on unnecessarily repeated serology tests in a cardiovascular surgery department: A time series analysis. BMC Health Serv Res 2010; 10:70
3. Chaboyer W, Kendall E, Kendall M, et al. Transfer out of intensive care: A qualitative exploration of patient and family perceptions. Aust Crit Care 2005; 18:138–141, 143–145
4. Leith BA. Transfer stress and medical intensive care patients and family members. Dynamics 2001; 12:22–27
5. Li P, Stelfox HT, Ghali WA. A prospective observational study of physician handoff for intensive-care-unit-to-ward patient transfers. Am J Med 2011; 124:860–867
6. National Transition of Care Coalition: Improving Transitions of Care. Washington, DC, National Transition of Care Coalition, 2008
7. Pronovost PJ, Miller MR, Dorman T, et al. Developing and implementing measures of quality of care in the intensive care unit. Curr Opin Crit Care 2001; 7:297–303
8. Rhodes A, Moreno RP, Azoulay E, et al.; Task Force on Safety
and Quality of European Society of Intensive Care Medicine (ESICM): Prospectively defined indicators to improve the safety
and quality of care for critically ill patients: A report from the Task Force on Safety
and Quality of the European Society of Intensive Care Medicine (ESICM). Intensive Care Med 2012; 38:598–605
9. Lane-Fall MB, Pascual JL, Peifer HG, et al. A partially structured postoperative handoff protocol improves communication in 2 mixed surgical intensive care units: Findings from the Handoffs and Transitions in Critical Care (HATRICC) Prospective Cohort Study. Ann Surg 2020;271:484–493
10. Santhosh L, Lyons PG, Rojas JC, et al. Characterising ICU-ward handoffs at three academic medical centres: Process and perceptions. BMJ Qual Saf 2019; 28:627–634
11. de Grood C, Job McIntosh C, Boyd JM, et al. Identifying essential elements to include in intensive care unit to hospital ward transfer summaries: A consensus methodology. J Crit Care 2019; 49:27–32
12. Boyd JM, Roberts DJ, Parsons Leigh J, et al. Administrator perspectives on ICU-to-ward transfers and content contained in existing transfer tools: A cross-sectional survey. J Gen Intern Med 2018; 33:1738–1745
13. Brown KN, Leigh JP, Kamran H, et al. Transfers from intensive care unit to hospital ward: A multicentre textual analysis of physician progress notes. Crit Care 2018; 22:19
14. Clinical Handover and Patient Safety
. 2005Sydney, NSW, Australia, Australian Council for Safety
and Quality in Health Care,
15. Agency for Healthcare Research and Quality: Patient Safety
Primers: Handoffs and Signouts. 2016Rockville, MD, Agency for Healthcare Research and Quality,
16. Bell CM, Brener SS, Gunraj N, et al. Association of ICU or hospital admission with unintentional discontinuation of medications for chronic diseases. JAMA 2011; 306:840–847
17. Kohn LT, Corrigan JM, Donaldson MS; Institute of Medicine (US) Committee on Quality of Health Care in America: To Err Is Human: Building a Safer Health System. 2000, Washington, DC, The National Academies Press, p 312
18. Sauro KM, Soo A, Quan H, et al. Adverse events among hospitalized critically ill patients: A retrospective cohort study using administrative data. 2020; 58:38–44
19. Parshuram CS, Amaral AC, Ferguson ND, et al.; Canadian Critical Care Trials Group: Patient safety
, resident well-being and continuity of care with different resident duty schedules in the intensive care unit: A randomized trial. CMAJ 2015; 187:321–329
20. Ahmed AH, Giri J, Kashyap R, et al. Outcome of adverse events and medical errors in the intensive care unit: A systematic review and meta-analysis. Am J Med Qual 2015; 30:23–30
21. Hosein FS, Roberts DJ, Turin TC, et al. A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality
after patient discharge from intensive care. Crit Care 2014; 18:715
22. de Vries EN, Ramrattan MA, Smorenburg SM, et al. The incidence and nature of in-hospital adverse events: A systematic review. Qual Saf Health Care 2008; 17:216–223
23. Southern DA, Burnand B, Droesler SE, et al. Deriving ICD-10 codes for patient safety
indicators for large-scale surveillance using administrative hospital data. Med Care 2017; 55:252–260
24. Al-Jaghbeer MJ, Tekwani SS, Gunn SR, et al. Incidence and etiology of potentially preventable ICU readmissions. Crit Care Med 2016; 44:1704–1709
25. Parsons Leigh J, Brown K, Buchner D, et al. Protocol to describe the analysis of text-based communication in medical records for patients discharged from intensive care to hospital ward. BMJ Open 2016; 6:e012200
26. Baker GR, Norton PG, Flintoft V, et al. The Canadian Adverse Events Study: The incidence of adverse events among hospital patients in Canada. CMAJ 2004; 170:1678–1686
27. Brennan TA, Leape LL, Laird NM, et al. Incidence of adverse events and negligence in hospitalized patients: Results of the Harvard Medical Practice Study I. 1991. Qual Saf Health Care 2004; 13:145–151; discussion 151–152
28. Ong MS, Coiera E. A systematic review of failures in handoff communication during intrahospital transfers. Jt Comm J Qual Patient Saf 2011; 37:274–284
29. Horwitz LI, Meredith T, Schuur JD, et al. Dropping the baton: A qualitative analysis of failures during the transition from emergency department to inpatient care. Ann Emerg Med 2009; 53:701–710.e4
30. Farnoosh L, Hossein-Nejad H, Beigmohammadi MT, et al. Preparation and implementation of intrahospital transfer protocol for emergency department patients to decrease unexpected events. Adv J Emerg Med 2018; 2:e29
31. Apker J, Mallak LA, Gibson SC. Communicating in the “gray zone”: Perceptions about emergency physician hospitalist handoffs and patient safety
. Acad Emerg Med 2007; 14:884–894
32. Catchpole KR, de Leval MR, McEwan A, et al. Patient handover from surgery to intensive care: Using Formula 1 pit-stop and aviation models to improve safety
and quality. Paediatr Anaesth 2007; 17:470–478
33. Sauro KM, Quan H, Sikdar KC, et al. Hospital safety
among neurologic patients: A population-based cohort study of adverse events. Neurology 2017; 89:284–290
34. Thomas EJ, Studdert DM, Burstin HR, et al. Incidence and types of adverse events and negligent care in Utah and Colorado. Med Care 2000; 38:261–271
35. Cullen DJ, Sweitzer BJ, Bates DW, et al. Preventable adverse drug events in hospitalized patients: A comparative study of intensive care and general care units. Crit Care Med 1997; 25:1289–1297
36. Elliott M, Page K, Worrall-Carter L. Factors associated with post-intensive care unit adverse events: A clinical validation study. Nurs Crit Care 2014; 19:228–235
37. Forster AJ, Kyeremanteng K, Hooper J, et al. The impact of adverse events in the intensive care unit on hospital mortality
and length of stay. BMC Health Serv Res 2008; 8:259
38. Sari AB, Cracknell A, Sheldon TA. Incidence, preventability and consequences of adverse events in older people: Results of a retrospective case-note review. Age Ageing 2008; 37:265–269
39. Sousa P, Uva AS, Serranheira F, et al. Estimating the incidence of adverse events in Portuguese hospitals: A contribution to improving quality and patient safety
. BMC Health Serv Res 2014; 14:311
40. Localio AR, Weaver SL, Landis JR, et al. Identifying adverse events caused by medical care: Degree of physician agreement in a retrospective chart review. Ann Intern Med 1996; 125:457–464
41. Pronovost PJ, Miller MR, Wachter RM. Tracking progress in patient safety
: An elusive target. JAMA 2006; 296:696–699
42. Woo SA, Cragg A, Wickham ME, et al. Methods for evaluating adverse drug event preventability in emergency department patients. BMC Med Res Methodol 2018; 18:160
43. Zegers M, de Bruijne MC, Wagner C, et al. Adverse events and potentially preventable deaths in Dutch hospitals: Results of a retrospective patient record review study. Qual Saf Health Care 2009; 18:297–302
44. Michel P, Quenon JL, de Sarasqueta AM, et al. Comparison of three methods for estimating rates of adverse events and rates of preventable adverse events in acute care hospitals. BMJ 2004; 328:199
45. Hosein FS, Bobrovitz N, Berthelot S, et al. A systematic review of tools for predicting severe adverse events following patient discharge from intensive care units. Crit Care 2013; 17:R102
46. Rosenberg AL, Watts C. Patients readmitted to ICUs*: A systematic review of risk factors and outcomes. Chest 2000; 118:492–502
47. Ho KM, Dobb GJ, Lee KY, et al. The effect of comorbidities on risk of intensive care readmission during the same hospitalization: A linked data cohort study. J Crit Care 2009; 24:101–107
48. Lee H, Lim CW, Hong HP, et al. Efficacy of the APACHE II score at ICU discharge in predicting post-ICU mortality and ICU readmission in critically ill surgical patients. Anaesth Intensive Care 2015; 43:175–186
49. Laupacis A, Sekar N, Stiell IG. Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA 1997; 277:488–494
50. Zegers M, De Bruijne MC, Spreeuwenberg P, et al. Variation in the rates of adverse events between hospitals and hospital departments. Int J Qual Health Care 2011; 23:126–133
51. Curry LA, Brault MA, Linnander EL, et al. Influencing organisational culture to improve hospital performance in care of patients with acute myocardial infarction: A mixed-methods intervention study. BMJ Qual Saf 2018; 27:207–217
52. Wunsch H. Is there a starling curve for intensive care? Chest 2012; 141:1393–1399
53. Town JA, Churpek MM, Yuen TC, et al. Relationship between ICU bed availability, ICU readmission, and cardiac arrest in the general wards. Crit Care Med 2014; 42:2037–2041
54. Stelfox HT, Lane D, Boyd JM, et al. A scoping review of patient discharge from intensive care: Opportunities and tools to improve care. Chest 2015; 147:317–327
55. Aiken LH, Clarke SP, Sloane DM, et al. Effects of hospital care environment on patient mortality and nurse outcomes. J Nurs Adm 2009; 39:S45–S51
56. Gershengorn HB, Harrison DA, Garland A, et al. Association of intensive care unit patient-to-intensivist ratios with hospital mortality
. JAMA Intern Med 2017; 177:388–396
57. McLaughlin N, Leslie GD, Williams TA, et al. Examining the occurrence of adverse events within 72 hours of discharge from the intensive care unit. Anaesth Intensive Care 2007; 35:486–493
58. Niven DJ, Bastos JF, Stelfox HT. Critical care transition programs and the risk of readmission or death after discharge from an ICU: A systematic review and meta-analysis. Crit Care Med 2014; 42:179–187
59. Stelfox HT, Bastos J, Niven DJ, et al. Critical care transition programs and the risk of readmission or death after discharge from ICU. Intensive Care Med 2016; 42:401–410