FAILURE TO RESCUE IN SURGICAL PATIENTS: A REVIEW FOR ACUTE CARE SURGEONS (2024)

Table 1.

Selected FTR definitions.

Hospital and Surgeon Volume

Multiple studies have demonstrated a strong negative correlation between volume and FTR rates. (30, 39) The focus of these works has been primarily on hospital volume (i.e., number of cases, patients, or beds hospital-wide). For example, Ghaferi et al analyzed Medicare data on upper GI cancer patients from 2005–2007. When hospitals were separated into quintiles by volume and the highest and lowest-volume groups were compared, there were small differences in complication rates (39% vs 43%), but considerably more dramatic differences in FTR rates (13% vs 30%). (40) Others have also considered surgeon volume. (20, 21) Buettner and colleagues analyzed nine years’ worth of data from the National Inpatient Sample (NIS) on hepatic surgery patients, finding correlations both between FTR and surgeon volume and between FTR and hospital volume. However, they note that surgeon volume had a much more significant effect. (3). The association with hospital volume may have to do with infrastructure, formal or informal care protocols, or with nursing staff who are familiar with particular patient populations, procedures, and complications. The association with surgeon volume may indicate that a surgeon that performs a procedure more often may have more experience with the common postoperative complications that require timely attention.

Nursing Factors – Staffing, Education, and Work Environment

One of the most consistently demonstrated hospital factors associated with FTR rates is nursing ratios. Multiple studies have suggested that higher nurse-to-patient ratios are protective against FTR events. (30, 41, 42) In one of the largest studies addressing this topic, Sheetz and colleagues performed a logistic regression analysis on data from approximately 2 million Medicare patients that had undergone one of 6 “high risk” general and vascular operations. In addition to teaching status, technology, and number of ICU beds, nursing ratios were found to be correlated with FTR rates. This study found that 12–57% of observed variation in FTR could be attributed to these hospital factors. The authors suggested that “micro” factors such as culture and climate might account for the remainder of the variation. (43)

Large-scale institutional factors can only go so far, and providers’ timely attention to issues and ability to communicate concerns may also have an impact on outcomes. Friese et al demonstrated this in a 2008 study of nursing factors associated with complications, mortality, and FTR. In addition to showing associations between FTR and both staffing ratios and education level, they found significant differences in all three outcomes when hospitals were grouped by nurse practice environment. The practice environment was evaluated using the Practice Environment Scale of the Nursing Work Index (PES-NWI), a scale composed of questions regarding hospital qualities such as opportunities for leadership, adequate support, and collegial relationships with physicians. (44) This supports the idea that “micro” factors may be a significant contributor to FTR.

Teaching Status

The question of housestaff and whether their presence is helpful or harmful has been debated. One might expect that trainees, given a lower level of experience, may provide inferior care. Alternatively, a housestaff team is often more present on the wards, and teaching hospital status may be a marker for other domains of high-quality care. Though the presence of surgical housestaff was found to be a detrimental factor by Silber et al, (1) subsequent studies have suggested that a hospital’s teaching status may in fact be protective. (10, 43, 45) It is possible that there are confounding effects here, given that teaching hospitals tend to be those with other high-quality features. (45) In a direct comparison of outcomes after colectomy in teaching vs non-teaching hospitals, Ko et al failed to show a difference in mortality or FTR rates. They did show statistically significant differences in particular complications, some higher in teaching hospitals and some higher in non-teaching hospitals, but none were clinically significant and this may have had more to do with large sample size (150,000 patients) than any clinically relevant differences. (8)

Intensive Care

Another institutional factor that has been shown to be associated with FTR rates is the adequate presence of intensive care unit (ICU) facilities and staff. Some of the studies cited above have demonstrated this relationship. (10, 43) A 2014 study examining this issue designated FTR “outliers” by using Medicare “Hospital Compare” data and determining which hospitals’ FTR rates had 95% confidence intervals (CIs) that lay entirely above or below the national FTR 95% CI. Clinicians at these hospitals were then surveyed regarding ICU characteristics. Many of the hypothesized characteristics did not achieve significance when top- and bottom- performers were compared (i.e. ECMO capabilities, CLABSI rates as a proxy for ICU quality). However, the presence of an intensivist on the rapid response team (RRT) and the presence of an internist on the ICU care team were both found to be more common in high performing institutions. (46)

Technology

Beginning with Silber, many authors have hypothesized that high-technology hospitals would have lower FTR rates, but results have been inconsistent. Silber found no correlation, (1) but Ghaferi et al. found technology index to be negatively associated with FTR. (30) It remains unclear whether the observed associations are due to technology per se, or whether there are contributions from other hospital, provider, and patient characteristics that accompany “high technology” hospitals and those who have access to them. The complex interactions of race, insurance status, and hospital resources with complication and FTR rates has only recently begun to be investigated on a detailed level. (22)

Staffing

Some have suggested that increased FTR rates are partially attributable to inadequate staffing during certain hours. A “weekend effect,” or a phenomenon in which patients experience poor outcomes when presenting at night or on weekends, has been explored. A reduced overall number of providers or the lack of an in-house attending physician at these times may contribute. Metcalfe et al explored this in the emergency general surgery population in 2018. Using approximately 1.3 million admissions’ worth of data from the NIS 2012–2013, they found a marginal increase in complications, mortality, and FTR on weekends. However, this was a very large sample and these findings may lack clinical significance (complications: 15.1% vs 14.9%; mortality: 1.5% vs 1.3%; FTR: 6.2% vs 5.9%). Additionally, the authors note that other factors may contribute, including that the patient mix that presents on the weekends may be more acutely ill (otherwise they may wait until Monday) or that coding may be suboptimal over the weekend. (23) Others have suggested that a “weekend effect” – or lack thereof – may be the mechanism behind the well-established negative correlation between teaching status and FTR rates, given the increasing chance of 24/7 resident coverage at a teaching institution. Evidence for this thus far is weak. (30)

Patient Factors

Although institutional factors contribute more strongly to FTR rates than to complication or mortality rates, patient factors contribute strongly to all three outcomes. Age has been repeatedly demonstrated to contribute to FTR, particularly in trauma and acute care surgery. In multiple studies, Sheetz et al demonstrated a significant increase in FTR rates in elderly populations (>75 years) following emergent general or vascular surgery. (47, 48) A similar concept has been demonstrated in the trauma population, (49) and in fact, a large study using the National Trauma Databank (NTDB) has shown a stepwise increase in FTR rates with increasing age. (50) Other investigators have focused not on age per se but on the role of “frailty,” as defined by the Trauma Specific Frailty Index (TSFI) or Risk Analysis Index (RAI). Increased rates of FTR are associated with increased frailty. (51, 52) Though age and frailty are “patient factors,” institutions can modify protocols and providers can increase their attentiveness to these patients in an effort to improve FTR rates. (53)

Insurance status also contributes to FTR in trauma and emergency surgery. After controlling for other factors, uninsured patients are more likely to experience an FTR event than insured ones. Multiple explanations for this have been suggested, including that uninsured individuals experience bias, implicit or explicit, on an individual level. Alternatively, it has been noted that uninsured patients tend to cluster at low-resource hospitals (a known risk factor for FTR) and that uninsured individuals may not have the means to address problems in an elective fashion and thus present emergently when the disease process is further along. (22, 54)

Applicability in Trauma and Emergency Surgery

The original FTR metric was created in the setting of common elective procedures (open cholecystectomy and transurethral prostatectomy). (1) As the metric has become more widely recognized and more widely used, some have raised the question of whether certain populations should be handled differently. Though overall trends similar to those described above have been demonstrated in trauma, (28) this population is fundamentally different from the elective surgical population. First, many trauma patients are managed non-operatively. Realizing that the pool of patients considered in the traditional FTR metric consisted entirely of postoperative patients, some have advocated considering whether or not trauma patients underwent an operation. (37)

Secondly, the question of precedence – the proportion of deaths that are preceded by a complication – has been raised in trauma. While elective FTR events have a nearly 100% precedence rate, in trauma – a field in which deaths secondary to injury are common – the rates range from 20–55% percent. Unreported complications may partially explain the wide range of reported values (24), but the question of precedence rates in emergency general surgery has yet to be explored.

A closely related issue is preventability. While most of the currently published FTR literature does not examine the preventability of cases, a few studies have leveraged existing peer review processes. (38, 55, 56) In trauma populations, the majority of FTR events are not deemed to be preventable, which may limit the utility of the FTR metric to measure outcomes that are subject to modification by the institution.

The emergency general surgery (EGS) population also differs from the elective surgical population. Mehta et al reported that unlike elective surgery, in which hospital factors contribute most, individual surgeon volume may have a stronger impact in EGS. One of these studies not only reported volume but also reported level of surgeon experience, as determined by years since medical school graduation. Most of the low EGS volume surgeons were senior surgeons. The conclusion the authors reach is that regardless of experience, having dedicated emergency general surgeons who face these situations regularly is important. They have also demonstrated that this effect is particularly important in elderly patients. (20, 21) Further investigation is needed in this area.

“Failure-to-pursue rescue” in emergency surgery (57)

Events which meet the technical definition of FTR may not represent the intention of the metric. Scarborough et al examined the role of “Do Not Resuscitate” (DNR) status in apparent FTR rates and found that in older patients undergoing emergency general surgery, complication rates were similar, but FTR rates differed (56.7% in DNR patients vs 41.4% in full-code patients). Some of all of this discrepancy may be attributable to patient disease or to patients’ desires regarding resuscitation. Additionally, providers may be inclined to be generally less aggressive in DNR patients, even prior to the resuscitation phase. (57)

Multiple complications

The traditional FTR metric only accounts for one complication. If there are multiple occurrences, only the first one is counted. However, many authors have theorized that death often occurs after a cascade of ever-more-severe complications. Wakeam et al used 7 years of National Surgical Quality Improvement Program (NSQIP) data to demonstrate that risk of a complication was highest among patients who had already developed a previous complication. The most common secondary complications were the logical extensions of the index complications – for example, wound dehiscence after a surgical site infection or cardiac arrest after myocardial infarction – indicating a failure to halt the progression from index complication to death. (58) Following this, Massarweh and colleagues used large-scale Veterans Affairs Surgical Quality Improvement Program (VASQIP) data on “low-risk” operations to demonstrate that the mortality rate rises with the number of complications a patient experiences, in a stepwise fashion. (59) If we are to use FTR data as a metric for improvement – in other words, if we are to focus on decreasing FTR rates – this type of information, which gives more detail about the progression from index complication to death, is valuable.

Additionally, though some make the distinction between “major” and “minor” complications, the nature of the first complication (i.e. cardiovascular vs pulmonary vs infectious) is often not considered. It has been shown that this does matter, particularly in elderly populations undergoing emergency general surgery. For this reason, increasingly granular information is important in order to most effectively use data on FTR events in a given institution. For example, if providers realize that infectious and pulmonary etiologies are particularly high-risk initial complications in the elderly, as shown by Sheetz et al, institutional protocols can be created for rapid intervention in these scenarios. (47)

Improving the Response: The Efferent Limb

Perhaps the best-studied intervention is the Rapid Response Team (RRTs), whose primary purpose it is to respond to complications in a timely manner and facilitate escalation of care should it be deemed necessary. Composition of such teams can vary but often consist of a critical care physician, critical care nurse, and respiratory therapist at a minimum. Despite the lack of strong evidence secondary to the inherent difficulty in studying this type of intervention, the existing literature supports the idea that such teams are beneficial. (41, 61) It has also been shown that the inclusion of an intensivist on the RRT is associated with a reduction in FTR rates. (46) Continuing along the conceptual pathway, some have focused on the phase of care after the patient has been transferred to the ICU and suggest that attention be focused on decreasing FTR rates in that setting. (62)

Recognition: The Afferent Limb

Thus far, we have discussed evaluation of FTR events, risk factors for FTR, and some of the efforts to improve the clinical response to a complication. In order for such handling of complications to be effective, however, the complication must be recognized in a timely manner. Few studies of FTR events have addressed the recognition of a complication and impending deterioration. A review by Taenzer et al. summarizes some of the approaches to improvement on this limb of the circuit. (61) One has been retrospective examination of FTR events, similar to some of the other material described here, in an attempt to identify the factors that put patients at risk. Risk Scoring Systems incorporating some of this information have been created, such as the Modified Early Warning Score (MEWS). These have not been shown to be reliable in separating those who will deteriorate from those who will not. (63) Similarly, continuous patient monitors in settings outside of the ICU have been associated with alarm fatigue without increasing sensitivity to complications. (64, 65) Other electronic systems for providing recommendations based on physiologic and laboratory data have been shown to improve care in a general sense, though it is unclear whether these reliably increase detection of postoperative complication. (66–68)

Within the ICU, telemedicine and eICU systems are increasingly used to provide another layer of safeguard against undetected complications. In such systems, an intensivist is responsible for monitoring patients in an ICU from a remote location, via computer connection. (69–71) There are multiple varieties of such monitoring. In the centralized monitoring model, continuous physiologic and laboratory data are transmitted to the remote intensivist. In the virtual consultant model, adjuncts (robot- or cart-based video communication hardware) are used for the intensivist to “round on” patients and provide input. The former variety is more extensively used and more extensively studied, though both appear to show an improvement in outcomes. (70) A decrease in mortality and hospital-level economic benefit has been demonstrated with centralized monitoring systems in both medical and surgical ICU patients. (69) Qualitative studies have reported provider satisfaction with such systems, though some warn that remote intensivists may at times make ill-advised recommendations if not provided with an appropriate amount of clinical context. (71) Though this is all promising, no decrease in failure-to-rescue events has been demonstrated with eICU or telemedicine interventions.

Some have begun to explore big data solutions for identification of deteriorating patients. A systematic review published earlier this year summarized findings from 6 studies on Electronic Medical Record (EMR) based algorithmic detection systems for acute lung injury (ALI), ventilator-associated lung injury, SIRS, and septic shock in ICU patients. (72) One of the reviewed studies (73) showed improved detection of ALI relative to clinicians. The remainder failed to show improvement, one specifically showed no change in implementation of sepsis therapy with an algorithmic system, (74) and most showed a poor positive predictive value (PPV) of such systems (30–60% in 4 of the 6 studies). Not all of the included studies explicitly had a comparator group. A team at the Mayo clinic used a split-sample design to derive and validate an EMR-based system to sepsis detection, demonstrating good sensitivity (80%) and specificity (96%). More importantly, per chart review, their system detected instances that were not detected initially by clinicians. (75) Though no improvement in FTR rates has ever been shown with such technology, this type of system may be useful in the future.

Information technology is also being used to detect adverse events after they have occurred, for the sake of review and improvement. Methods using International Classification of Diseases (ICD) codes have been used for this purpose for some time. The use of microbiology data and pharmacy data for detection of infection detection and adverse drug reactions, respectively, is also well-established. More recently, more advanced methods, including natural language processing for analysis of narrative progress notes, are being employed for detection of a broader range of events. (76)

While RRTs have been shown to be valuable, their success depends in part on their “afferent limbs.” Several Australian studies have examined the effects of a delay in RRT (known as a ‘medical emergency team’ or MET in Australia) involvement. In two retrospective studies investigating this, a significant increase in mortality was demonstrated (OR = 2.1 in one study, OR = 3.1 in the other) with a delay in MET call, which was defined a priori as >30 minutes between the first documentation of a physiologic derangement and the call. (77, 78) In a third, prospective study, the same group looked at factors associated with mortality in MET patients and found a delay in the call (this time defined as >1hr) to be one of only two significantly influential factors (the other one being DNR status). (79)

What becomes apparent through the findings described above is that the time course of a given complication is important in determining how feasible it is to prevent a failure-to-rescue event. In order for us to improve upon our performance in the afferent limb, there must be a period of time during which the complication is recognizable AND slow-progressing enough to act upon. Though many post-surgical complications fulfill both of these criteria, there are several that may predispose to mortality that do not. For example, patients may develop asymptomatic deep venous thromboses (DVTs), which cannot be acted upon if providers do not know they are there. Or, a patient could develop a new ventricular arrhythmia (without preceding complication) that proves to be fatal too quickly for the patient to be rescued.

There are many remaining questions regarding how best to reduce institutional FTR rates. For instance, what factors are associated with provider failures in complication recognition, and what barriers exist to escalation of a deteriorating patient’s care? Recent literature has started to explore these phenomena. The concept of “inattentional blindness,” in which people do not notice the unexpected, has been discussed in the nursing literature as it relates to responses to a decompensating patient. (80) In a related concept, the term “afferent limb failure” has been used to describe situations in which Rapid Response Systems were not appropriately activated. (81) Johnston and colleagues have identified delays in “escalation of care” as a significant predictor of FTR events. (39) In two qualitative studies, the group has attempted to identify hazardous steps in the process of escalation and describe the reasons for failure. Identified issues are the use of pagers (causing a delay in communication between nurses and physicians), lack of expertise in junior residents, and lack of availability/approachability of senior residents, leading to recommendations for better communication, junior resident education, and an improved working environment. (82, 83)

1. Silber JH, Williams SV, Krakauer H, Schwartz JS. Hospital and patient characteristics associated with death after surgery: A study of adverse occurrence and failure to rescue. Med Care. 1992;30(7):615–27. [PubMed] [Google Scholar]

2. Hafeez Bhatti AB, Dar FS, Qureshi AI, Khan NY, Zia HH, Khan EUD, Khan NA, Salih M, Shah NH. Failure to rescue in living donor liver transplantation: Patterns and predictors. Int J Surg. 2017;44:281–6. [PubMed] [Google Scholar]

3. Buettner S, Gani F, Amini N, Spolverato G, Kim Y, Kilic A, Wagner D, Pawlik TM. The relative effect of hospital and surgeon volume on failure to rescue among patients undergoing liver resection for cancer. Surgery2016;159(4):1004–12. [PubMed] [Google Scholar]

4. Spolverato G, Ejaz A, Hyder O, Kim Y, Pawlik TM. Failure to rescue as a source of variation in hospital mortality after hepatic surgery. Br J Surg. 2014;101(7):836–46. [PubMed] [Google Scholar]

5. Farjah FFailure-to-Rescue in Thoracic Surgery. Thorac Surg Clin. 2017;27(3):257–66. [PubMed] [Google Scholar]

6. Farjah F, Backhus L, Cheng A, Englum B, Kim S, Saha-Chaudhuri P, Wood DE, Mulligan MS, Varghese TK. Failure to rescue and pulmonary resection for lung cancer. J Thorac Cardiovasc Surg. 2015;149(5):1365–73. [PubMed] [Google Scholar]

7. Kozower BD. Failure-to-rescue: An evolving quality metric. J Thorac Cardiovasc Surg. 2015;149(5):1247–8. [PubMed] [Google Scholar]

8. Ko A, Aquino L, Melo N, Alban RF. Surgical outcomes and failure-to-rescue events after colectomy in teaching hospitals: a nationwide analysis. Am J Surg. 2016;212(6):1133–9. [PubMed] [Google Scholar]

9. Tevis SE, Carchman EH, Foley EF, Heise CP, Harms BA, Kennedy GD. Does anastomotic leak contribute to high failure-to-rescue rates?Ann Surg. 2016;263(6):1148–51. [PubMed] [Google Scholar]

10. Henneman D, Van Leersum NJ, Ten Berge M, Snijders HS, Fiocco M, Wiggers T, Tollenaar RAEM, Wouters MWJM. Failure-to-rescue after colorectal cancer surgery and the association with three structural hospital factors. Ann Surg Oncol. 2013;20(11):3370–6. [PubMed] [Google Scholar]

11. Weledji EP, Verla V. Failure to rescue patients from early critical complications of oesophagogastric cancer surgery. Ann Med Surg. 2016;7:34–41. [PMC free article] [PubMed] [Google Scholar]

12. Busweiler LA, Henneman D, Dikken JL, Fiocco M, van Berge Henegouwen MI, Wijnhoven BP, van Hillegersberg R, Rosman C, Wouters MW, van Sandick JW, et al. Failure-to-rescue in patients undergoing surgery for esophageal or gastric cancer. Eur J Surg Oncol. 2017;43(10):1962–9. [PubMed] [Google Scholar]

13. Liou DZ, Serna-Gallegos D, Mirocha J, Bairamian V, Alban RF, Soukiasian HJ. Predictors of Failure to Rescue After Esophagectomy. Ann Thorac Surg. 2018;105(3):871–8. [PubMed] [Google Scholar]

14. Tamirisa NP, Parmar AD, Vargas GM, Mehta HB, Molly Kilbane E, Hall BL, Pitt HA, Riall TS. Relative contributions of complications and failure to rescue on mortality in older patients undergoing pancreatectomy. Ann Surg. 2016;263(2):385–91. [PMC free article] [PubMed] [Google Scholar]

15. Varley PR, Geller DA, Tsung A. Factors influencing failure to rescue after pancreaticoduodenectomy: a National Surgical Quality Improvement Project Perspective. J Surg Res. 2017;214:131–9. [PubMed] [Google Scholar]

16. Gribben JL, Ilonzo N, Neifert S, Leitman IM. Predictors of reoperation and failure to rescue in bariatric surgery. JSLS. 2018;22(1). [PMC free article] [PubMed] [Google Scholar]

17. Cramm SL, Waits SA, Englesbe MJ, Bucuvalas JC, Horslen SP, Mazariegos GV, Soltys KA, Anand R, Magee JC. Failure to Rescue as a Quality Improvement Approach in Transplantation: A First Effort to Evaluate This Tool in Pediatric Liver Transplantation. Transplantation. 2016;100(4):801–7. [PubMed] [Google Scholar]

18. Simpson KR. Failure to Rescue: Implications for Evaluating Quality of Care During Labor and Birth. J perinat neonat nur. 2005;19(1):24–34. [PubMed] [Google Scholar]

19. Silber JH, Arriaga AF, Niknam BA, Hill AS, Ross RN, Romano PS. Failure-to-Rescue After Acute Myocardial Infarction. Med Care. 2018;56(5):416–23. [PubMed] [Google Scholar]

20. Mehta A, Dultz LA, Joseph B, Canner JK, Stevens K, Jones C, Haut ER, Efron DT, Sakran JV. Emergency general surgery in geriatric patients: A statewide analysis of surgeon and hospital volume with outcomes. J Trauma Acute Care Surg. 2018;84(6):864–75. [PubMed] [Google Scholar]

21. Mehta A, Efron DT, Canner JK, Dultz L, Xu T, Jones C, Haut ER, Higgins RSD, Sakran JV. Effect of Surgeon and Hospital Volume on Emergency General Surgery Outcomes. J Am Coll Surg. 2017;225(5):666–75.e2. [PubMed] [Google Scholar]

22. Metcalfe D, Castillo-Angeles M, Olufajo OA, Rios-Diaz AJ, Salim A, Haider AH, Havens JM. Failure to rescue and disparities in emergency general surgery. J Surg Res. 2018;231:62–8. [PubMed] [Google Scholar]

23. Metcalfe D, Castillo-Angeles M, Rios-Diaz AJ, Havens JM, Haider A, Salim A. Is there a “weekend effect” in emergency general surgery?J Surg Res. 2018;222:219–24. [PubMed] [Google Scholar]

24. Holena DN, Earl-Royal E, Delgado MK, Sims CA, Pascual JL, Hsu JY, Carr BG, Reilly PM, Wiebe D. Failure to rescue in trauma: Coming to terms with the second term. Injury. 2016;47(1):77–82. [PMC free article] [PubMed] [Google Scholar]

25. Holena DN, Kaufman EJ, Delgado MK, Wiebe DJ, Carr BG, Christie JD, Reilly PM. A metric of our own: Failure to rescue after trauma. J Trauma Acute Care Surg. 2017;83(4):698–704. [PMC free article] [PubMed] [Google Scholar]

26. Ghaferi AA, Birkmeyer JD, Dimick JB. Complications, Failure to Rescue, and Mortality With Major Inpatient Surgery in Medicare Patients. Ann Surg. 2009;250(6):1029–34. [PubMed] [Google Scholar]

27. Ghaferi AA, Birkmeyer JD, Dimick JB. Variation in hospital mortality associated with inpatient surgery. N Engl J Med. 2009;361(14):1368–75. [PubMed] [Google Scholar]

28. Glance LG, Dick AW, Meredith JW, Mukamel DB. Variation in hospital complication rates and failure-to-rescue for trauma patients. Ann Surg. 2011;253(4):811–6. [PubMed] [Google Scholar]

29. Massarweh NN, Kougias P, Wilson MA. Complications and failure to rescue after inpatient noncardiac surgery in the veterans affairs health system. JAMA Surg. 2016;151(12):1157–65. [PubMed] [Google Scholar]

30. Ghaferi AA, Dimick JB. Variation in Mortality After High-Risk Cancer Surgery. Failure to Rescue. Surg Oncol Clin N Am. 2012;21(3):389–95. [PubMed] [Google Scholar]

31. Needleman J, Buerhaus PI, Vanderboom C, Harris M. Using Present-On-Admission Coding to Improve Exclusion Rules for Quality Metrics: The Case of Failure-to-Rescue. Med Care. 2013;51(8):722–30. [PubMed] [Google Scholar]

32. Kaufman EJ, Earl-Royal E, Barie PS, Holena DN. Failure to rescue after infectious complications in a statewide trauma system. Surg Infect. 2017;18(2):89–98. [PMC free article] [PubMed] [Google Scholar]

33. Silber JH, Romano PS, Rosen AK, Wang Y, Even-Shoshan O, Volpp KG. Failure-to-rescue comparing definitions to measure quality of care. Med Care. 2007;45(10):918–25. [PubMed] [Google Scholar]

34. Needleman J, Buerhaus P, Mattke S, Stewart M, Zelevinsky K. Nurse-staffing levels and the quality of care in hospitals. N Engl J Med. 2002;346(22):1715–22. [PubMed] [Google Scholar]

35. Rosen AK, Rivard P, Zhao S, Loveland S, Tsilimingras D, Christiansen CL, Elixhauser A, Romano PS. Evaluating the patient safety indicators: How well do they perform on Veterans Health Administration data?Med Care. 2005;43(9):873–84. [PubMed] [Google Scholar]

36. Almoudaris AM, Burns EM, Mamidanna R, Bottle A, Aylin P, Vincent C, Faiz O. Value of failure to rescue as a marker of the standard of care following reoperation for complications after colorectal resection. Br J Surg. 2011;98(12):1775–83. [PubMed] [Google Scholar]

37. Almoudaris AM, Mamidanna R, Faiz O. Failure to rescue in trauma patients: Operative interventions must be considered. Ann Surg. 2014;259(6). [PubMed] [Google Scholar]

38. Kuo LE, Kaufman E, Hoffman RL, Pascual JL, Martin ND, Kelz RR, Holena DN. Failure-to-rescue after injury is associated with preventability: The results of mortality panel review of failure-to-rescue cases in trauma. Surgery. 2017;161(3):782–90. [PMC free article] [PubMed] [Google Scholar]

39. Johnston MJ, Arora S, King D, Bouras G, Almoudaris AM, Davis R, Darzi A. A systematic review to identify the factors that affect failure to rescue and escalation of care in surgery. Surgery. 2015;157(4):752–63. [PubMed] [Google Scholar]

40. Ghaferi AA, Birkmeyer JD, Dimick JB. Hospital volume and failure to rescue with high-risk surgery. Med Care. 2011;49(12):1076–81. [PubMed] [Google Scholar]

41. Schmid A, Hoffman L, Happ MB, Wolf GA, DeVita M. Failure to rescue: A literature review. J Nurs Adm. 2007;37(4):188–98. [PubMed] [Google Scholar]

42. Wakeam E, Hevelone ND, Maine R, Swain J, Lipsitz SA, Finlayson SRG, Ashley SW, Weissman JS. Failure to rescue in safety-net hospitals availability of hospital resources and differences in performance. JAMA Surg. 2014;149(3):229–35. [PubMed] [Google Scholar]

43. Sheetz KH, Dimick JB, Ghaferi AA. Impact of hospital characteristics on failure to rescue following major surgery. Ann Surg. 2016;263(4):692–7. [PMC free article] [PubMed] [Google Scholar]

44. Friese CR, Lake ET, Aiken LH, Silber JH, Sochalski J. Hospital nurse practice environments and outcomes for surgical oncology patients. Health Serv Res. 2008;43(4):1145–63. [PMC free article] [PubMed] [Google Scholar]

45. McKee M, Coles J, James P. ‘Failure to rescue’ as a measure of quality of hospital care: The limitations of secondary diagnosis coding in English hospital data. J Public Health Med. 1999;21(4):453–8. [PubMed] [Google Scholar]

46. Wakeam E, Asafu-Adjei D, Ashley SW, Cooper Z, Weissman JS. The association of intensivists with failure-to-rescue rates in outlier hospitals: Results of a national survey of intensive care unit organizational characteristics. J Crit Care. 2014;29(6):930–5. [PubMed] [Google Scholar]

47. Sheetz KH, Krell RW, Englesbe MJ, Birkmeyer JD, Campbell DA Jr, Ghaferi AA. The importance of the first complication: Understanding failure to rescue after emergent surgery in the elderly. J Am Coll Surg. 2014;219(3):365–70. [PMC free article] [PubMed] [Google Scholar]

48. Sheetz KH, Waits SA, Krell RW, Campbell DA Jr, Englesbe MJ, Ghaferi AA. Improving mortality following emergent surgery in older patients requires focus on complication rescue. Ann Surg. 2013;258(4):614–7. [PMC free article] [PubMed] [Google Scholar]

49. Earl-Royal E, Kaufman EJ, Hsu JY, Wiebe DJ, Reilly PM, Holena DN. Age and preexisting conditions as risk factors for severe adverse events and failure to rescue after injury. J Surg Res. 2016;205(2):368–77. [PMC free article] [PubMed] [Google Scholar]

50. Barmparas G, Martin MJ, Wiegmann DA, Catchpole KR, Gewertz BL, Ley EJ. Increased age predicts failure to rescue. Am Surg. 2016;82(11):1073–9. [PubMed] [Google Scholar]

51. Joseph B, Phelan H, Hassan A, Orouji Jokar T, O’Keeffe T, Azim A, Gries L, Kulvatunyou N, Latifi R, Rhee P. The impact of frailty on failure-to-rescue in geriatric trauma patients: A prospective study. J Trauma Acute Care Surg. 2016;81(6):1150–5. [PubMed] [Google Scholar]

52. Shah R, Attwood K, Arya S, Hall DE, Johanning JM, Gabriel E, Visioni A, Nurkin S, Kukar M, Hochwald S, et al. Association of frailty with failure to rescue after low-risk and high-risk inpatient surgery. JAMA Surg. 2018;153(5). [PMC free article] [PubMed] [Google Scholar]

53. Ghaferi AA, Dimick JB. Importance of teamwork, communication and culture on failure-to-rescue in the elderly. Br J Surg. 2016;103(2):e47–e51. [PMC free article] [PubMed] [Google Scholar]

54. Bell TM, Zarzaur BL. Insurance status is a predictor of failure to rescue in trauma patients at both safety net and non-safety net hospitals. J Trauma Acute Care Surg. 2013;75(4):728–33. [PubMed] [Google Scholar]

55. Coates M, Pillado E, Kim J, Vasak R, Yule A, Kim DY. Role of preventability in redefining failure to rescue among major trauma patients. JAMA Surg. 2017;152(11):1083–4. [PMC free article] [PubMed] [Google Scholar]

56. Ingraham AM, Greenberg CC. Failure to rescue and preventability: Striving for the impossible?Surgery2017;161(3):793–4. [PubMed] [Google Scholar]

57. Scarborough JE, Pappas TN, Bennett KM, Lagoo-Deenadayalan S. Failure-to-pursue rescue: Explaining excess mortality in elderly emergency general surgical patients with preexisting do-not-resuscitate orders. Ann Surg. 2012;256(3):453–61. [PubMed] [Google Scholar]

58. Wakeam E, Hyder JA, Jiang W, Lipsitz SA, Finlayson S. Risk and patterns of secondary complications in surgical inpatients. JAMA Surg. 2015;150(1):65–73. [PubMed] [Google Scholar]

59. Massarweh NN, Anaya DA, Kougias P, Bakaeen FG, Awad SS, Berger DH. Variation and Impact of Multiple Complications on Failure to Rescue after Inpatient Surgery. Ann Surg. 2017;266(1):59–65. [PubMed] [Google Scholar]

60. Buist MD, Jarmolowski E, Burton PR, Bernard SA, Waxman BP, Anderson J. Recognising clinical instability in hospital patients before cardiac arrest or unplanned admission to intensive care. A pilot study in a tertiary-care hospital. Med J Australia. 1999;171(1):22–5. [PubMed] [Google Scholar]

61. Taenzer AH, Pyke JB, McGrath SP. A review of current and emerging approaches to address failure-to-rescue. Anesthesiology. 2011;115(2):421–31. [PubMed] [Google Scholar]

62. Chung JJ, Earl-Royal EC, Delgado MK, Pascual JL, Reilly PM, Wiebe DJ, Holena DN. Where we fail: Location and timing of failure to rescue in trauma. Am Surg. 2017;83(3):250–6. [PMC free article] [PubMed] [Google Scholar]

63. Gao H, McDonnell A, Harrison DA, Moore T, Adam S, Daly K, Esmonde L, Goldhill DR, Parry GJ, Rashidian A, et al. Systematic review and evaluation of physiological track and trigger warning systems for identifying at-risk patients on the ward. Intensive Care Med. 2007;33(4):667–79. [PubMed] [Google Scholar]

64. Larson TS, Brady WJ. Electrocardiographic monitoring in the hospitalized patient: a diagnostic intervention of uncertain clinical impact. Am J Emerg Med. 2008;26(9):1047–55. [PubMed] [Google Scholar]

65. Watkinson PJ, Barber VS, Price JD, Hann A, Tarassenko L, Young JD. A randomised controlled trial of the effect of continuous electronic physiological monitoring on the adverse event rate in high risk medical and surgical patients. Anaesthesia. 2006;61(11):1031–9. [PubMed] [Google Scholar]

66. Garg AX, Adhikari NKJ, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: A systematic review. JAMA. 2005;293(10):1223–38. [PubMed] [Google Scholar]

67. 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(1):10–20. [PubMed] [Google Scholar]

68. Tepas JJ III, Rimar JM, Hsiao AL, Nussbaum MS. Automated analysis of electronic medical record data reflects the pathophysiology of operative complications. Surgery. 2013;154(4):918–26. [PubMed] [Google Scholar]

69. Breslow MJ, Rosenfeld BA, Doerfler M, Burke G, Yates G, Stone DJ, Tomaszewicz P, Hochman R, Plocher DW. Effect of a multiple-site intensive care unit telemedicine program on clinical and economic outcomes: an alternative paradigm for intensivist staffing. Crit Care Med. 2004;32(1):31–8. [PubMed] [Google Scholar]

70. Ramnath VR, Ho L, Maggio LA, Khazeni N. Centralized monitoring and virtual consultant models of tele-ICU care: a systematic review. Telemed E Health. 2014;20(10):936–61. [PubMed] [Google Scholar]

71. Khunlertkit A, Carayon P. Contributions of tele–intensive care unit (Tele-ICU) technology to quality of care and patient safety. J Crit Care. 2013;28(3):315. e1-. e12. [PubMed] [Google Scholar]

72. Despins LA. Automated Deterioration Detection Using Electronic Medical Record Data in Intensive Care Unit Patients: A Systematic Review. CIN - Computers Informatics Nursing. 2018;36(7):323–30. [PubMed] [Google Scholar]

73. Herasevich V, Yilmaz M, Khan H, Hubmayr RD, Gajic O. Validation of an electronic surveillance system for acute lung injury. Intensive Care Med. 2009;35(6):1018–23. [PMC free article] [PubMed] [Google Scholar]

74. Hooper MH, Weavind L, Wheeler AP, Martin JB, Gowda SS, Semler MW, Hayes RM, Albert DW, Deane NB, Nian H, et al. Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit. Crit Care Med. 2012;40(7):2096–101. [PMC free article] [PubMed] [Google Scholar]

75. Harrison AM, Thongprayoon C, Kashyap R, Chute CG, Gajic O, Pickering BW, Herasevich V. Developing the surveillance algorithm for detection of failure to recognize and treat severe sepsis. Mayo Clin Proc. 2015;90(2):166–75. [PMC free article] [PubMed] [Google Scholar]

76. Bates DW, Evans RS, Murff H, Stetson PD, Pizzifferri L, Hripcsak G. Detecting adverse events using information technology. J Am Med Inform Assoc. 2003;10(2):115–28. [PMC free article] [PubMed] [Google Scholar]

77. Downey AW, Quach JL, Haase M, Haase-Fielitz A, Jones D, Bellomo R. Characteristics and outcomes of patients receiving a medical emergency team review for acute change in conscious state or arrhythmias. Crit Care Med. 2008;36(2):477–81. [PubMed] [Google Scholar]

78. Quach JL, Downey AW, Haase M, Haase-Fielitz A, Jones D, Bellomo R. Characteristics and outcomes of patients receiving a medical emergency team review for respiratory distress or hypotension. J Crit Care. 2008;23(3):325–31. [PubMed] [Google Scholar]

79. Calzavacca P, Licari E, Tee A, Egi M, Haase M, Haase-Fielitz A, Bellomo R. A prospective study of factors influencing the outcome of patients after a Medical Emergency Team review. Intensive Care Med. 2008;34(11):2112–6. [PubMed] [Google Scholar]

80. Jones A, Johnstone MJ. Inattentional blindness and failures to rescue the deteriorating patient in critical care, emergency and perioperative settings: Four case scenarios. Aust Crit Care. 2017;30(4):219–23. [PubMed] [Google Scholar]

81. Trinkle RM, Flabouris A. Documenting Rapid Response System afferent limb failure and associated patient outcomes. Resuscitation. 2011;82(7):810–4. [PubMed] [Google Scholar]

82. Johnston M, Arora S, Anderson O, King D, Behar N, Darzi A. Escalation of Care in Surgery: A Systematic Risk Assessment to Prevent Avoidable Harm in Hospitalized Patients. Ann Surg. 2015;261(5):831–8. [PubMed] [Google Scholar]

83. Johnston M, Arora S, King D, Stroman L, Darzi A. Escalation of care and failure to rescue: A multicenter, multiprofessional qualitative study. Surgery. 2014;155(6):989–94. [PubMed] [Google Scholar]