Abstract
Introduction
Never events (NE) and hospital-acquired conditions (HAC) after surgery have been designated as quality metrics in health-care by the Centres for Medicare and Medicaid Services (CMS).
Methods
The Nationwide Inpatient Sample (NIS) database 2002–2012 was used to identify patientswho underwent kidney transplant. Multivariate analysis using logistic regression was used to identify outcomes and risk factors of HAC and NE after transplantation; however, we were limited by using a retrospective database missing some important variables specified for the kidney transplant, such as some operative factors, donor factors, and cold and warm ischemia times.
Results
Among 35 058 patients who underwent kidney transplant, there were 11 NEs, all of which were due to retained foreign bodies. Among HAC after surgery, falling was the most common (44.9%), followed by poor glycemic control (21.7%), vascular catheter-associated infection (21%), and catheter-associated urinary tract infection (8%). HAC and NE after surgery lead to a significant increase in mortality (adjusted odds ratio [AOR] 2.49; p=0.04), hospitalization length (13 vs. 7 days; p<0.01), and total hospital charges ($231 801 vs. $146 717; p<0.01). A significantly higher risk of HAC or NE was seen for patients who had more loss of function before surgey (AOR 3.25; p<0.01) and patients expected to have higher postoperative mortality before operation (AOR 1.62; p=0.03).
Conclusions
Despite the limitations of the study, we found HAC and NE significantly increase mortality, hospitalization length, and total hospital charges of kidney transplant patients. Quality improvement initiatives should target HAC and NE in order to successfully reduce or prevent these events.
Introduction
The quality and safety of patient care in hospitals are important aims of National Quality Forum (NQF) in the U.S.1,2 As adverse, serious events that are largely preventable, never events (NE) and hospital-acquired conditions (HAC) are reliable measurements of the quality and safety of patient care.1,2 NEs and HACs also have a significant financial impact on the U.S. healthcare system.3 It has been estimated that payments for surgical NE amounted to over $1.3 billion from 1990–2010.4 Eliminating surgical NEs is necessary to limit harm to patients.4 Understanding the impact and frequency of these conditions can help to design the best preventative strategies.
NE was defined in 2006 by NQF, which includes 28 reportable events in healthcare.2 The list includes obvious unacceptable errors; however, not all the events are indicative of obvious negligence.2 A goal of quality improvement is the reduction of NEs to zero. In this line, Centres for Medicare and Medicaid Services (CMS) adopted the non-reimbursement policy for some of the events with the name of “non-reimbursable serious hospital-acquired conditions” in order to motivate hospitals to accelerate improvement of patient safety.2 Investigating patient characteristics and operative factors with the events may help improve current prevention strategies. Using a nationwide database, this study aims to investigate predictors and outcomes of NE and HAC after kidney transplantation using appropriate events for the kidney transplant procedure according to both NQF and CMS lists.
Methods
An analysis of the Nationwide Inpatient Sample (NIS) database from 2002–2012 was used in this study. NIS is an inpatient care database according to hospital discharge data in the U.S. acquired by the Healthcare Cost and Utilization Project (HCUP) of the Agency for Healthcare Research and Quality, Rockville, MD. It is an annually compiled database that consists of approximately 8 million inpatient stays from approximately 1000 hospitals each year.5 Informed consent is obtained from individual patients within the individual hospitals’ patient consent forms. For the purposes of this study, NIS was queried using the Ninth Revision of the International Classification of Disease (ICD-9-CM) procedure code of 55.69 to identify kidney transplantation cases. ICD-9 diagnosis codes, which were reported in principal diagnosis of patients, were also used to identify relevant diagnoses of patients. This study investigated NEs and HACs after kidney transplantation using the ICD-9 diagnosis codes, which were reported as the second to 25th diagnosis of patients in the database. Details of the codes used to identify NE and HAC are reported in Table 1. The definition of complications (NE and HAC) were made according to ICD-9 diagnosis codes, which is available online.6
Table 1.
Hospital-acquired conditions and never events identification codes
| Diagnosis | ICD-9 codes |
|---|---|
| Hospital-acquired conditions (HAC) | |
| Air embolism | 999.1 |
| Blood incompatibility | 999.60, 999.61, 999.62, 999.63, 999.69 |
| Pressure ulcer stages III & IV | 707.23, 707.24 |
| Falls and trauma | 800–829, 830–839, 850–854, 925–929, 940–949, 991–994 |
| Catheter-associated urinary tract infection | 996.64 |
| Vascular catheter-associated infection | 999.31, 999.32, 999.33 |
| Poor glycemic control | 250.10–250.13, 250.20–250.23, 251.0, 249.10–249.11, 249.20–249.21 |
| Never events | |
| Retained foreign body | E871.0, E871.9, 998.4, 998.7 |
| Wrong operation on correct patient | E876.5 |
| Wrong operation intended for another patient | E876.6 |
| Correct operation on wrong body part/site | E876.7 |
Patient variables include demographic data (age, sex, and race), patient diagnosis, comorbidities (hypertension, coagulopathy, and diabetes mellitus), hospitalization length, total hospital charges, and admission type (elective vs. non-elective). Patient loss of function before surgery and risk of mortality (mild, moderate, major, and extreme) were according to the classification of the NIS database.5 The primary endpoints were rates of NE and HAC after kidney transplantation. Secondary endpoints were predictors and outcomes of NE and HAC after kidney transplantation. Risk adjusted analysis was performed to investigate predictors and outcomes of NE and HAC.
Statistical analysis
Data was analyzed using the Statistical Package for Social Sciences (SPSS) software, Version 22 (SPSS Inc., Chicago, IL, U.S.). The main analysis was multivariate analysis using logistic regression. The associations of NE and HAC with mortality and morbidity of patients were examined using a multivariable logistic regression model. We included all the variables of the study as covariates in the model. The estimated adjusted odds ratio (AOR) with a 95% confidence interval (CI) was calculated for each correlation. Statistical hypotheses were tested using p<0.05 as the level of statistical significance.
Results
We sampled 35 058 patients who underwent kidney transplant from 2002–2012 accordng to the NIS database. Of these, 60.2% were male. The median age of patient was 50 years. Also, the majority of patients were Caucasian (53.8%). Deficiency anemia (41.2%) and fluid and electrolyte disorders (31.8%) were the most common reported comorbid conditions of patients. The median hospitalization length of patients was six days. The most common reported reasons of renal failure and need for kidney transplant were hypertension (42%) and diabetes (34.7%). Demographics and clinical characteristics of patients are shown in Table 2.
Table 2.
Demographics and clinical characteristics of patients underwent kidney transplant with or without never events (NE) and hospital-acquired conditions (HAC)
| Variables | Patients with NE or HAC | Patients without NE or HAC | p | |
|---|---|---|---|---|
| Age | Mean±standard deviation (years) | 48±16 | 47±15 | <0.01 |
| Median (years) | 50 | 50 | ---- | |
|
| ||||
| Sex | Female | 41.5% | 39.8% | 0.67 |
|
| ||||
| Race | White | 65.9% | 44.6% | <0.01 |
| Black or African American | 20.3% | 18.3% | 0.64 | |
| Hispanic | 8.9% | 12.8% | 0.04 | |
| Asian | 0.8% | 3.8% | 0.97 | |
| Other or unknown | 4.1% | 20.5% | 0.04 | |
|
| ||||
| Comorbidity | Fluid and electrolyte disorders | 37.7% | 31.8% | 0.12 |
| Coagulopathy | 12.3% | 7.1% | 0.01 | |
| Deficiency anemia | 40.4% | 41.2% | 0.84 | |
| Diabetes | 47.6% | 23.9% | <0.01 | |
| Liver disease | 5.5% | 3.1% | 0.08 | |
| Weight loss | 2.1% | 1.2% | 0.38 | |
| Hypertension | 14.4% | 10.8% | 0.16 | |
| Chronic pulmonary disease | 5.4% | 5.6% | 0.94 | |
| Obesity | 5.5% | 7.5% | 0.34 | |
| Congestive heart failure | 5.4% | 4.9% | 0.74 | |
| Peripheral vascular disorders | 7.5% | 4.8% | 0.12 | |
|
| ||||
| Preoperative expected mortality | Minor likelihood of dying | 7.9% | 32.5% | <0.01 |
| Moderate likelihood of dying | 47.4% | 46.7% | <0.01 | |
| Major likelihood of dying | 30.7% | 17.8% | <0.01 | |
| Extreme likelihood of dying | 14% | 3.1% | <0.01 | |
|
| ||||
| Preoperative loss of function | Minor loss of function | 3.5% | 15.7% | <0.01 |
| Moderate loss of function | 27.2% | 46.6% | <0.01 | |
| Major loss of function | 45.6% | 32.2% | <0.01 | |
| Extreme loss of function | 23.7% | 5.5% | <0.01 | |
|
| ||||
| Indication of kidney transplant | Hypertension | 23.1% | 42.1% | <0.01 |
| Diabetes mellitus | 63.9% | 34.6% | <0.01 | |
| Previous kidney transplant failure | 5.4% | 6.1% | 0.72 | |
| Polycystic kidney disease | 1.4% | 3.3% | 0.19 | |
| Lupus erythematous | 0% | 1.5% | 0.13 | |
| Other | 6.1% | 12.4% | 0.09 | |
|
| ||||
| Admission type | Elective | 54.4% | 54.8% | 0.92 |
| Non-elective | 45.6% | 45.2% | 0.92 | |
|
| ||||
| Hospitalization length | Mean±standard deviation (days) | 13±11 | 7±7 | <0.01 |
| Median (days) | 8 | 6 | <0.01 | |
|
| ||||
| Total hospital charges | Mean±standard deviation | $231 801 ±216 093 | $146 717 ±97 759 | <0.01 |
| Median | $156 647 | $124 184 | <0.01 | |
|
| ||||
| Outcomes | Mortality | 1.4% | 0.5% | <0.01 |
| Overall morbidity | 45.6% | 24.2% | <0.01 | |
Among patients who underwent kidney transplant, 11 (0.03%) had NEs and all of the events were due to retained foreign bodies. Overall, 138 patients had postoperative HAC, of which falling was the most common event (44.9%), followed by poor glycemic control (21.7%), vascular catheter-associated infection (21%), catheter-associated urinary tract infection (8%), stages III and IV pressure ulcers (2.9%), and ABO incompatible blood transfusion (2.2%) (Fig. 1).
Fig. 1.
Never events and hospital-acquired conditions after kidney transplant.
The mortality and morbidity of patients who underwent kidney transplantation were 0.5% and 24.3%, respectively. Patients with HAC or NE had significantly higher mortality (1.4% vs. 0.5%; p=0.04) and morbidity (45.6% vs. 24.2%; p<0.01). HAC and NE after surgery were significantly associated with an increased mean length of stay (13 vs. 7 days; p<0.01) and hospital charges of patients ($231 801 vs. $146 717; p<0.01). Also, patients with NE or HAC had a higher risk of unplanned reoperation (AOR 1.92; p=0.04), prolonged ileus (AOR 2.28; p<0.01), pneumonia (AOR 3.31; p<0.01), acute myocardial infarction (AOR 2.72; p<0.01), and respiratory failure (AOR 3.69; p<0.01) (Table 3).
Table 3.
Risk adjusted analysis of postoperative complications of patients with or without never events (NE) and hospital-acquired conditions (HAC)
| Complications | Patients with NE or HAC | Patients without NE or HAC | Adjusted odds ratio | 95% confidence interval | p |
|---|---|---|---|---|---|
| Mortality | 1.4% | 0.5% | 2.49 | 1.01–10.31 | 0.04 |
| Overall morbidity* | 45.6% | 24.2% | 2.44 | 1.74–3.42 | <0.01 |
| Transplanted kidney failure or rejection | 10.2% | 8.2% | 1.23 | 0.71–2.12 | 0.45 |
| Renal vascular complications | 2.7% | 0.6% | 3.83 | 1.38–10.64 | 0.01 |
| Wound disruption | 3.4% | 0.5% | 5.74 | 2.28–14.41 | <0.01 |
| Hemorrhagic complications | 7.5% | 5.2% | 1.28 | 0.68–2.41 | 0.43 |
| Ureter complications | 4.1% | 3.4% | 1.19 | 0.52–2.71 | 0.67 |
| Unplanned reoperation | 5.4% | 2.2% | 1.92 | 1.01–4.02 | 0.04 |
| Prolonged ileus | 10.9% | 4.7% | 2.28 | 1.34–3.88 | <0.01 |
| Urinary tract infection | 15.6% | 3.9% | 4.03 | 2.54–6.41 | <0.01 |
| Wound infection | 2.7% | 0.8% | 2.63 | 0.94–7.38 | 0.06 |
| Pneumonia | 4.1% | 1.1% | 3.31 | 1.43–7.67 | <0.01 |
| Hospitalization >30 days | 8.2% | 1.1% | 6.16 | 3.22–11.79 | <0.01 |
| Acute myocardial infarction | 6.1% | 2% | 2.72 | 1.36–5.44 | <0.01 |
| Acute respiratory failure | 4.8% | 1.1% | 3.69 | 1.68–8.12 | <0.01 |
| Deep vein thrombosis | 0% | 0.3% | 0.99 | 0.99–1.00 | 0.51 |
Includes: Transplanted kidney failure or rejection, renal vascular complications, wound disruption, hemorrhagic complications, ureter complications, unplanned reoperation, prolonged ileus, urinary tract infection, unplanned reoperation, wound infection, pneumonia, hospitalization more than 30 days, acute myocardial infarction, acute respiratory failure, deep vein thrombosis.
Risk adjusted analysis of factors associated with postoperative NE and HAC are reported in Table 4. A significantly higher risk of HAC or NE events was seen for patients who had a severe disease before surgery (AOR 3.25; p<0.01) and patients who were expected to have more loss of function before surgey (AOR 1.62; p=0.03). When investigating patients who had catheter-related urinary tract infection, factors such as age (AOR 0.96; CI 0.92–0.99; p=0.04), female gender (AOR 15.48; CI 1.92–124.41; p=0.01), and severity of loss of function before surgey (AOR 9.70; CI 1.01–94.88; p=0.04) were significantly associated with catheter-related urinary tract infection. Also, severity of loss of function before surgey was significantly associated with falling (AOR 3.14; CI 1.48–6.67; p<0.01), retained foreign body (AOR 7.58; CI 1.24–46.28; p=0.02), and vascular catheter-associated infection (AOR 6.82; CI 1.22–38.05; p=0.02). Poor glycemic control was significantly associated with patient’s age (AOR 0.94; CI 0.91–0.98; p<0.01).
Table 4.
Risk-adjusted analysis of factors associated with postoperative never events and hospital-acquired conditions
| Variables | Adjusted odds ratio | 95% confidence interval | p | |
|---|---|---|---|---|
| Age | Age | 0.98 | 0.97–0.99 | 0.03 |
|
| ||||
| Sex | Female | 1.10 | 0.75–1.61 | 0.61 |
|
| ||||
| Comorbidity | Obesity | 0.48 | 0.19–1.20 | 0.12 |
| Coagulopathy | 0.76 | 0.38–1.44 | 0.45 | |
| Hypertension | 0.89 | 0.50–1.58 | 0.70 | |
| Diabetes mellitus | 0.99 | 0.59–1.66 | 0.99 | |
| Fluid and electrolyte abnormalities | 0.78 | 0.52–1.18 | 0.24 | |
| Chronic lung disease | 0.94 | 0.41–2.16 | 0.89 | |
| Weight loss | 0.77 | 0.18–3.19 | 0.72 | |
| Deficiency anemia | 0.89 | 0.60–1.32 | 0.57 | |
| Congestive heart failure | 0.50 | 0.20–1.26 | 0.14 | |
| Peripheral vascular disorders | 1.05 | 0.51–2.24 | 0.87 | |
| Liver disease | 1.32 | 0.53–3.29 | 0.54 | |
|
| ||||
| Preoperative expected mortality | Low or moderate likelihood of dying | Reference | Reference | Reference |
| High or extreme high likelihood of dying | 1.62 | 1.03–2.55 | 0.03 | |
|
| ||||
| Preoperative loss of function | Minor or moderate loss of function | Reference | Reference | Reference |
| Major or extreme loss of function | 3.25 | 1.95–5.41 | <0.01 | |
Discussion
This study found a significant increase in mortality, morbidity, hospitalization length, and total hospital charges of patients with NEs and HACs after kidney transplant. Also, the risks of other postoperative complications, such as prolonged ileus, pneumonia, acute myocardial infarction, and respiratory failure, increase in presense of NE and HAC. We reinforce the literatures reports on the severity effect of NE and HAC events on patient outcomes, as well as the significant increase in total hospital charges related to the events.7–9
Our study results show the severity of loss of function before surgey is a reliable factor to find patients at high risk for postoperative NE and HAC (Table 4). We found the risk of NE and HAC for patients with major or extreme loss of function before surgey is more than three times that of patients with minor or moderate loss of function before surgery. Patient characteristics have been reported as important predictors of the occurrence of a NE in the literature.10 Although preventive sterategies should be done for all surgical patients, some high-risk patients may benefit from frequent assessments to decrease the risk of NE during hospitalization. For example, creating a mandatory checklist that should be filled out frequently during hospitalization by the responsible surgeon may be useful in high-risk patients.2
We found a significant association between catheter-related urinary tract infection and age, female gender, and severity of loss of function before surgey, which is in line with literaure reports;11,12 however, we could not evaluate correlation between urinary stent and length of using urinary catheter and urinay tract infection. It is estimated that up to 69% of catheter-related urinary tract infection can be prevented using appropriate infection prevention strategies, such as the removal of the catheter as soon as possible or avoidance of its use.12–14 Considering 38% of physicians were not aware of the status of urinary catheter use for their patients,12,15 reminder systems, including face-to-face reminders involving staff nurses and virtual reminders involving the use of electronic devices, may help decrease the risk of catheter-related urinary tract infection.12
Our study results show falling is the most common preventable HAC in kidney transplant patients. The overall reported rate of fall after general surgery procedures is 1.6% in literature;16 we found a rate of 0.2% postoperative falls for kidney transplant patients — lower than for general surgeries. Although a fall seems like a simple event, in the literature it represents a failure of multiple physiological systems and also a marker for increased perioperative mortality and morbidity and postoperative delirium.16–19 Recognition of fall risk factors and identifying high-risk patients for fall will help design postoperative fall prevention programs. Factors like older age, functional dependence, and lower albumin levels have been reported to be associated with falls.16 We also found a higher risk of fall for patients who had more loss of function before surgey. Interestingly, 66% of patients who fell had diabetes as a result of kiney failure. Diabetes with peripheral neuropathy can increase chance of fall after surgery. Minimizing polypharmacy and avoiding individual medications that increase the risk of delirium, increasing the presence of family members or sitters at the bedside, minimizing environmental hazards, and occupational and physical therapy training in high-risk patients, especialy in diabetetic patients with peripheral neuropathy, may decrease the risk of fall in high-risk patients.
We found vascular catheter-associated infection as the third most common HAC after kidney transplant. It has been estimated that there are 15 million central vascular catheter days for patients hospitalized at intensive care units each year in the U.S.20,21 There are multiple studies that addressed catheter-related bloodstream infections in literature.20,21 Factors such as the duration of catheterization and use of a semipermeable transparent dressing have been reported to be independently associated with positive cultures of catheters.22 We found a significantly higher risk of vascular catheter-associated infection in patients who had more loss of function before surgey. Following guidelines for the prevention of intravascular catheter-related infections can decrease the risk of vascular catheter-associated infection in surgical patients.21 Some evidence-based recommendations include: educating and designating trained healthcare personnel and assessing their knowledge and adherence to guidelines, correcting selection of catheters and sites, hand hygiene and aseptic techniques, maximal sterile barrier precautions, and appropriate catheter site dressing regimens.21
Our study results show poor glycemic control is the second most common HAC in kidney transplant patient and it is reversly associated with patient’s age. Perioperative hyperglycemia has been reported as an adverse outcome predictor in surgical patients even in the non-diabetic population.23,24 It has been reported that postoperative blood glucose greater than 140 mg/dL is present in as many as 40% of non-cardiac surgery patients and 25% of those patients have a blood glucose level greater than 180 mg/dL.24 Checking the blood glucose in the morning of surgery in patients with and without a history of diabetes is recommended.23 Blood glucose level of 150 mg/dl has been reported as the cutoff point for increasing risks of mortality, morbidity, and hospitalization length, particularly in those who do not have a prior diagnosis of diabetes.24 Perioperative immunosupresive medications, such as corticosteroids and prograf, also can increase blood sugar of transplanted patients and make control of blood sugar in such patients difficult; however, further studies are indicated to determine whether strict perioperative blood glucose management improves clinical outcomes in transplanted patients.
Study limitations
There are limitations to the study. Detection of adverse events in the NIS database is limited to the ICD-9-CM coding system and coding error is possible.25,26 Despite our attempts to adjust for all possible confounders, we could not measure some variables that contribute to patient outcomes, such as warm and cold ischemia time, presence of urinary stent, effects of perioperative immunosupresive medications, and length of use of urinary catheter. The NIS dataset misses some potentially important explanatory variables, such as anatomic or laboratory data. Also, The NIS has no ability to follow patient outcomes longitudinally. Despite these limitations, the advantage of using the NIS database is the broad national geographic representation across all regions of the country and also the possibility of reporting weighted results as national outcomes.
Conclusion
HAC and NE after kidney transplantation are uncommon; however, they are associated with a significant increase in mortality, morbidity, hospitalization length, and hospital charges. Quality improvement initiatives should target HAC and NE in order to successfully reduce or prevent these events. The severity of loss of function before surgey is a reliable factor to identify patients at high risk for postoperative NE and HAC. Falling is the most common preventable HAC in kidney transplant patients. The severity of loss of function before surgey is significantly associated with falling, retained foreign body, catheter-related urinary tract infection, and vascular catheter-associated infection. The risks of poor glycemic control and catheter-related urinary tract infection significantly increase in the elderly. Following guideline recommendations in the prevention of HAC and NE may decrease the rates of NE and HAC in high-risk patients.
Footnotes
Competing interests: The authors report no competing personal or financial interests.
This paper has been peer-reviewed.
References
- 1.Teufack SG, Campbell P, Jabbour P, et al. Potential financial impact of restriction in “never event” and peri-procedural hospital-acquired condition reimbursement at a tertiary neurosurgical centre: A single-institution prospective study. J Neurosurg. 2010;112:249–56. doi: 10.3171/2009.7.JNS09753. https://doi.org/10.3171/2009.7.JNS09753. [DOI] [PubMed] [Google Scholar]
- 2.Lembitz A, Clarke TJ. Clarifying “never events and introducing “always events”. Patient Saf Surg. 2009;3:26. doi: 10.1186/1754-9493-3-26. https://doi.org/10.1186/1754-9493-3-26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Nero DC, Lipp MJ, Callahan MA. The financial impact of hospital-acquired conditions. J Health Care Finance. 2012;38:40–9. [PubMed] [Google Scholar]
- 4.Mehtsun WT, Ibrahim AM, Diener-West M, et al. Surgical never events in the United States. Surgery. 2013;153:465–72. doi: 10.1016/j.surg.2012.10.005. https://doi.org/10.1016/j.surg.2012.10.005. [DOI] [PubMed] [Google Scholar]
- 5.HCUP Nationwide Inpatient Sample (NIS) Healthcare Cost and Utilization Project (HCUP) Agency for Healthcare Research and Quality; Rockville, MD: 2000–2010. [Accessed October 19, 2017]. Available at www.hcup-us.ahrq.gov/nisoverview.jsp. [Google Scholar]
- 6.The International Classification of Diseases Nr, Clinical Modification: ICD-9-CM. 4th ed. Washington, DC: Services USDoHaH; 1991. Aahwidc. [Google Scholar]
- 7.Shah NK, Farber A, Kalish JA, et al. Occurrence of “never events” after major open vascular surgery procedures. J Vasc Surg. 2016;63:738–45.e728. doi: 10.1016/j.jvs.2015.09.024. https://doi.org/10.1016/j.jvs.2015.09.024. [DOI] [PubMed] [Google Scholar]
- 8.Wen T, He S, Attenello F, et al. The impact of patient age and comorbidities on the occurrence of “never events” in cerebrovascular surgery: An analysis of the Nationwide Inpatient Sample. J Neurosurg. 2014;121:580–6. doi: 10.3171/2014.4.JNS131253. https://doi.org/10.3171/2014.4.JNS131253. [DOI] [PubMed] [Google Scholar]
- 9.Wen T, Pease M, Attenello FJ, et al. Evaluation of effect of weekend admission on the prevalence of hospital-acquired conditions in patients receiving cervical fusions. World Neurosurg. 2015;84:58–68. doi: 10.1016/j.wneu.2015.02.028. https://doi.org/10.1016/j.wneu.2015.02.028. [DOI] [PubMed] [Google Scholar]
- 10.Fry DE, Pine M, Jones BL, et al. Patient characteristics and the occurrence of never events. Arch Surg. 2010;145:148–51. doi: 10.1001/archsurg.2009.277. https://doi.org/10.1001/archsurg.2009.277. [DOI] [PubMed] [Google Scholar]
- 11.Guggenbichler JP, Assadian O, Boeswald M, et al. Incidence and clinical implication of nosocomial infections associated with implantable biomaterials — catheters, ventilator-associated pneumonia, urinary tract infections. GMS Krankenhhyg Interdiszip. 2011;6 doi: 10.3205/dgkh000175. Doc18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Tambyah PA, Oon J. Catheter-associated urinary tract infection. Curr Opin Infect Dis. 2012;25:365–70. doi: 10.1097/QCO.0b013e32835565cc. https://doi.org/10.1097/QCO.0b013e32835565cc. [DOI] [PubMed] [Google Scholar]
- 13.Reed D, Kemmerly SA. Infection control and prevention: A review of hospital-acquired infections and the economic implications. Ochsner J. 2009;9:27–31. [PMC free article] [PubMed] [Google Scholar]
- 14.Rebmann T, Greene LR. Preventing ventilator-associated pneumonia: An executive summary of the Association for Professionals in Infection Control and Epidemiology, Inc, elimination guide. Am J Infect Control. 2010;38:647–9. doi: 10.1016/j.ajic.2010.08.004. https://doi.org/10.1016/j.ajic.2010.08.004. [DOI] [PubMed] [Google Scholar]
- 15.Saint S, Wiese J, Amory JK, et al. Are physicians aware of which of their patients have indwelling urinary catheters? Am J Med. 2000;109:476–80. doi: 10.1016/s0002-9343(00)00531-3. https://doi.org/10.1016/S0002-9343(00)00531-3. [DOI] [PubMed] [Google Scholar]
- 16.Church S, Robinson TN, Angles EM, et al. Postoperative falls in the acute hospital setting: Characteristics, risk factors, and outcomes in males. Am J Surg. 2011;201:197–202. doi: 10.1016/j.amjsurg.2009.12.013. https://doi.org/10.1016/j.amjsurg.2009.12.013. [DOI] [PubMed] [Google Scholar]
- 17.Robinson TN, Eiseman B, Wallace JI, et al. Redefining geriatric preoperative assessment using frailty, disability, and comorbidity. Ann Surg. 2009;250:449–55. doi: 10.1097/SLA.0b013e3181b45598. https://doi.org/10.1097/SLA.0b013e3181b45598. [DOI] [PubMed] [Google Scholar]
- 18.Dasgupta M, Dumbrell AC. Preoperative risk assessment for delirium after noncardiac surgery: A systematic review. J Am Geriatr Soc. 2006;54:1578–89. doi: 10.1111/j.1532-5415.2006.00893.x. https://doi.org/10.1111/j.1532-5415.2006.00893.x. [DOI] [PubMed] [Google Scholar]
- 19.Robinson TN, Raeburn CD, Tran ZV, et al. Postoperative delirium in the elderly: Risk factors and outcomes. Ann Surg. 2009;249:173–8. doi: 10.1097/SLA.0b013e31818e4776. https://doi.org/10.1097/SLA.0b013e31818e4776. [DOI] [PubMed] [Google Scholar]
- 20.Mermel LA. Prevention of intravascular catheter-related infections. Ann Intern Med. 2000;132:391–402. doi: 10.7326/0003-4819-132-5-200003070-00009. https://doi.org/10.7326/0003-4819-132-5-200003070-00009. [DOI] [PubMed] [Google Scholar]
- 21.O’Grady NP, Alexander M, Burns LA, et al. Guidelines for the prevention of intravascular catheter-related infections. Clin Infect Dis. 2011;52:e162–93. doi: 10.1093/cid/cir257. https://doi.org/10.1093/cid/cir257. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Richet H, Hubert B, Nitemberg G, et al. Prospective, multicentre study of vascular catheter-related complications and risk factors for positive central catheter cultures in intensive care unit patients. J Clin Microbiol. 1990;28:2520–5. doi: 10.1128/jcm.28.11.2520-2525.1990. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kwon S, Thompson R, Dellinger P, et al. Importance of perioperative glycemic control in general surgery: A report from the Surgical Care and Outcomes Assessment Program. Ann Surg. 2013;257:8–14. doi: 10.1097/SLA.0b013e31827b6bbc. https://doi.org/10.1097/SLA.0b013e31827b6bbc. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Frisch A, Chandra P, Smiley D, et al. Prevalence and clinical outcome of hyperglycemia in the perioperative period in noncardiac surgery. Diabetes Care. 2010;33:1783–8. doi: 10.2337/dc10-0304. https://doi.org/10.2337/dc10-0304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Lorence DP, Ibrahim IA. Benchmarking variation in coding accuracy across the United States. J Health Care Finance. 2003;29:29–42. [PubMed] [Google Scholar]
- 26.Berthelsen CL. Evaluation of coding data quality of the HCUP National Inpatient Sample. Top Health Inf Manage. 2000;21:10–23. [PubMed] [Google Scholar]