Melissa Camp, David C. Chang, Yiyi Zhang, Kristin Chrouser, Paul M. Colombani, Fizan Abdullah. Risk Factors and Outcomes for Foreign Body Left During a ProcedureAnalysis of 413 Incidents After 1 946 831 Operations in Children. Arch Surg. 2010;145(11):1085–1090. doi:10.1001/archsurg.2010.241
To determine risk factors and outcomes associated with a foreign body left during a procedure in a population of pediatric surgical patients.
The Nationwide Inpatient Sample and Kids' Inpatient Database were used to identify hospitalized pediatric surgical patients in the United States (aged 0-18 years) from 1988 to 2005.
After data from 1 946 831 hospitalizations in children were linked to the Agency for Healthcare Research and Quality Pediatric Quality Indicator (PDI) software, 413 pediatric patients with foreign bodies left during a procedure (PDI 3) were identified. A 1:3 matched case-control design was implemented with 413 cases and 1227 controls. Cases and controls were stratified into procedure categories based on diagnosis related group procedure codes.
Main Outcome Measures
To examine the relationship between PDI 3 and procedure category, as well as the outcomes of in-hospital mortality, length of stay, and total hospital charges.
Logistic regression analysis revealed a statistically significant higher odds of PDI 3 in the gynecology procedure category (odds ratio, 4.13; P = .01). Multivariable regression analysis revealed that patients with PDI 3 had an 8-day longer length of stay (95% confidence interval, 5.6-10.3 days; P < .001) and $35 681 higher total hospital charges (95% confidence interval, $22 358-$49 004; P < .001) but were not more likely to die (odds ratio, 1.07; P = .92).
Among pediatric surgical admissions, a foreign body left during a procedure was observed to occur with highest likelihood during gynecologic operations. The occurrence of this adverse event was associated with longer length of stay and greater total hospital charges, but not with increased mortality.
The Institute of Medicine report To Err Is Human: Building a Safer Health System, published in 1999, identified medical errors as a significant problem with respect to costs as well as morbidity and mortality.1- 3Crossing the Quality Chasm: A New Health System for the 21st Century, published by the Institute of Medicine in 2001, emphasized the importance of patient safety in addressing medical errors.2,4 The Agency for Healthcare Research and Quality (AHRQ), in response to To Err Is Human, developed a set of Patient Safety Indicators (PSIs) that were designed to identify adverse events occurring in the inpatient setting that are related to patient safety.2,3 The occurrence of a PSI event has been shown to be associated with worse clinical outcomes in terms of longer lengths of stay, higher rates of inpatient mortality, and higher total hospital charges in both adult and pediatric populations.2,3,5 By linking the PSIs to hospital administrative data, institutions can identify problem areas and then set priorities to address patient safety–related quality improvement efforts.3
The relevance of the PSIs to the pediatric population has been questioned. Sedman et al6 published a study in 2005 that concluded that when the PSIs were applied to children, not all of the events identified necessarily represented preventable events. In addition, they reported that 2 of the PSIs were inaccurate for use in the pediatric population. In 2006, AHRQ developed another set of patient safety indicators, known as the Pediatric Quality Indicators (PDIs), specifically designed for use in evaluating the quality and safety of health care in children.7,8 The PDIs, when linked to hospital inpatient administrative databases, can identify iatrogenic and other potentially preventable adverse events that occur in children. Using this new tool, institutions can develop a unique perspective on problems in the health care system and institute changes that can be implemented at a systems level. There are 13 PDIs.8
This study was deemed exempt by the Johns Hopkins Hospital institutional review board.
A retrospective analysis of a nonoverlapping combination of the Nationwide Inpatient Sample (NIS) and Kids' Inpatient Database (KID) from 1988 through 2005 was performed. Both databases have been developed as part of the Healthcare Cost and Utilization Project of the AHRQ. The NIS is an all-payer database that contains data on up to 8 million inpatient discharges from approximately 1000 hospitals across the United States each year. The NIS samples at the hospital level to represent a 20% sample of all community hospitals. Currently, data are available from 37 states.9 The KID contains a sample of pediatric (20 years or younger) discharges from all community, nonrehabilitation hospitals in states that participate in the Healthcare Cost and Utilization Project. The KID samples patient discharges using a systematic random sampling algorithm to select 10% of uncomplicated in-hospital births and 80% of complicated in-hospital births as well as other selected pediatric cases. The KID contains information from up to 36 states.10 Information collected on patients from both databases for this analysis included age at admission, sex, race, hospital ID code, diagnosis and procedure information, admission type, insurance status, mortality, length of stay (LOS), and total hospital charges. The PDIs were generated by the WinQI software from AHRQ11 and added to the databases.
Patients were initially selected from the NIS and KID if they had an age at admission of younger than 18 years and a procedure code corresponding to any type of surgical operation, according to the Appendix in the Pediatric Quality Indicators Technical Specifications Manual.12 The data from this group of pediatric surgical patients were then linked to the AHRQ PDIs.
Because of the large number of pediatric surgical patients and relatively small proportion of patients identified as having a foreign body left during a procedure (PDI 3) (0.02%), the decision was made to create a cohort including all cases but a smaller subset of controls. Cases were identified as patients with PDI 3 and potential controls were considered to be any of the pediatric surgical patients without PDI 3. Using Stata version 10.0,13 a 1:3 matched case-control program (fastmatch) was used to match 3 controls per case, with cases and controls matched on age (±2 years), race, sex, and hospital ID code. If more than 3 potential matched controls were available, 3 were randomly selected by the fastmatch program. Cases for which fewer than 3 matched controls were available (n = 11) were still included in the analysis with their corresponding number of matched controls.
A descriptive analysis was performed on cases and controls using variables of age, sex, race, admission type (emergent, urgent, or elective), admission source (emergency department, nonemergency department, or unknown), insurance status (uninsured or insured), and inpatient mortality. Summary statistics for LOS and total adjusted hospital charges were calculated. Total hospital charges were adjusted for inflation to reflect 2006 dollars.14 Comparisons between groups were performed using Pearson χ2 for categorical outcomes and a t test for continuous variables.
Diagnosis related group (DRG) procedure codes were used to classify cases and controls into the following procedure categories: cardiothoracic; endocrine; ears, nose, throat; gastrointestinal; gynecology; interventional; neurosurgery; ophthalmology; orthopedic; skin/soft tissue; spine; transplant; trauma/burns; urology; and vascular. A complete list of the DRG procedure codes and category assignments has been described previously.15 Because of the span of the analysis over 1988 to 2005, patients with DRG codes that are presently no longer valid were excluded from the procedure category stratified analysis. Rates of PDI 3 by procedure category were calculated using the entire unmatched data set. Comparisons between cases and controls by procedure category in the matched data set were performed using Pearson χ2.
A multiple logistic regression analysis was performed to examine the association between PDI 3 and procedure category in the matched data set, controlling for age, race, hospital region, hospital type, admission source, admission type, and insurance status. For analysis of outcome measures associated with PDI 3, a multiple logistic regression analysis was performed for mortality and multiple linear regression analyses were performed for LOS and total hospital charges. All regression analyses for outcomes measures controlled for procedure category, age, race, hospital region, hospital type, admission source, admission type, and insurance status.
All statistical analysis was performed using Stata version 10.0.13
From the NIS and KID databases from 1988 to 2005, 1 946 831 children aged 0 to 18 years were identified as having any type of surgical operation and 413 (0.02%) of these children were found to have PDI 3. Cases and controls were classified by DRG procedure codes into procedure categories. Because of DRG codes that are no longer valid, not all of the cases and controls were able to be classified into a procedure category. Taking the entire unmatched data set of 413 cases and 1 946 418 controls, 316 (76.51%) of the cases and 1 709 989 (87.85%) of the controls had valid DRG codes that allowed them to be classified into a procedure category. This subset was used to calculate rates of PDI 3 by procedure category, as shown in Table 1. Overall, the rate of PDI 3 was 0.18 event per 1000 pediatric surgical patient discharges. By procedure category, the highest rates of PDI 3 occurred in the transplant (0.97 event per 1000 pediatric surgical patient discharges), gynecology (0.96 event per 1000 pediatric surgical patient discharges), and vascular (0.75 event per 1000 pediatric surgical patient discharges) categories.
The 1:3 matched case-control program was used to create a cohort containing a smaller number of controls, and 395 of the 413 cases (patients with PDI 3) were successfully matched with exactly 3 controls (patients without PDI 3). Of the remaining 18 cases, 4 were matched with zero controls, 3 were matched with 1 control, 4 were matched with 2 controls, and 7 were matched with greater than 3 controls (from which 3 controls were randomly selected). All 413 cases, with their corresponding number of matched controls (0, 1, 2, or 3), were included in the matched case-control data set for a total of 1227 controls. Cases and controls were matched on age (±2 years), race, sex, and hospital ID code. As shown in Table 2, the mean age and proportions of sex and race were nearly identical in cases and controls (as expected because of matching on these variables in the creation of this data set). There was also no statistically significant difference between cases and controls with respect to insurance status (P = .67), admission source (P = .70), or admission type (P = .90).
Table 3 depicts the unadjusted outcome results for in-hospital mortality, LOS, and total hospital charges for cases and controls in the matched data set. Although mortality was higher for cases compared with controls, this difference did not achieve statistical significance (1.7% vs 0.7%; P = .16). The differences in mean LOS and mean total hospital charges between cases and controls were statistically significant. Mean LOS was longer for cases (7 vs 3 days; P <.001), and mean total hospital charges were higher for cases compared with controls ($89 415 vs $40 503; P <.001).
In the matched data set, 316 of the 413 cases (76.51%) and 1041 of the 1227 controls (84.84%) had valid DRG codes that allowed them to be classified into a procedure category. The highest proportion of PDI 3 cases occurred in the gastrointestinal (21.52%), cardiothoracic (16.14%), and orthopedic (12.97%) categories. These were also 3 of the 4 most common procedure categories overall, with gastrointestinal representing 25.28%, cardiothoracic representing 10.83%, and orthopedic representing 18.28% of the total procedures for cases and controls combined.
A multiple logistic regression analysis was performed to examine the association between PDI 3 and procedure category in the matched data set, controlling for age, race, admission type, admission source, hospital region, hospital type, and insurance status. The odds ratios (ORs) for PDI 3, by procedure category, are displayed in Table 4. There was a statistically significantly higher odds of PDI 3 compared with no PDI 3 in the gynecology (OR, 4.13; P = .01) procedure category. The cardiothoracic, vascular, endocrine, and trauma/burns procedure categories all had an OR greater than 1, but the corresponding P values did not achieve statistical significance. All of the remaining procedure categories had an OR less than 1, indicating a lower odds of PDI 3 compared with no PDI 3. Those categories with a statistically significant OR less than 1 included ears, nose, throat; gastrointestinal; orthopedic; and skin/soft tissue.
A logistic regression analysis for the outcome of mortality in the matched case-control data set revealed no difference in mortality between patients with PDI 3 compared with patients without PDI 3 (OR, 1.07; P = .92). Multiple linear regression analyses in the matched data set revealed that patients with PDI 3 had an 8-day longer LOS (95% confidence interval, 5.6-10.3 days; P < .001) and had $35 681 higher total hospital charges (95% confidence interval, $22 358-$49 004; P < .001) compared with patients without PDI 3. All regression analyses controlled for procedure category, age, race, hospital region, hospital type, admission source, admission type, and insurance status. The results of the regression analyses are displayed in Table 5.
Because gynecologic procedures had the highest OR for retained foreign body, a subset analysis was performed that revealed that 15 of 17 patients had ovarian cyst or cancer-related procedures, 1 of 17 had a cesarean section, and 1 of 17 had a procedure for pelvic adhesions.
The goal of this analysis was to determine the risk factors and outcomes associated with a foreign body left during a procedure (PDI 3) in a large representative sample of pediatric surgical patients. Given the relatively small proportion of patients identified as having PDI 3 (413 or 0.02%), the decision was made to create a cohort that included all 413 cases but a smaller subset of controls. Our rationale for choosing a 1:3 case to control matching design was based in part on an article published by Zhan and Miller16 in 2003 and has been previously described in our analysis of the risk factors and outcomes associated with PDI 1 (accidental puncture or laceration).15 Only 11 of the 413 PDI 3 cases (2.66%) were unable to be matched with the desired 3 controls. Since the purpose of the 1:3 matching was to create a smaller subset of appropriate controls, all cases were included in the matched data set with their corresponding number of matched controls.
Similar to our PDI 1 analysis, cases and controls were sorted into 15 different procedure categories based on DRG procedure codes.15 Because of DRG codes that are presently no longer valid, some of the cases and controls could not be classified into a procedure category and had to be excluded from this portion of the analysis. Unfortunately, a higher proportion of cases (23.49%) compared with controls (15.16%) had to be excluded because of invalid DRG codes. This difference, however, should create a bias toward the null when comparing cases with controls.
With respect to associations between foreign body left during a procedure and procedure category, cases of PDI 3 in the matched data set were most commonly associated with the gastrointestinal, cardiothoracic, and orthopedic procedure categories. These results were analogous to what we observed in our PDI 1 analysis.15 Multiple logistic regression identified the gynecology procedure category as having the highest likelihood of foreign body left during a procedure. Subset analysis of the gynecology-related incidents revealed that 15 of 17 patients had primary diagnoses and procedures relating to ovarian cysts or cancer.
With respect to outcomes, cases fared worse than controls. Regression analyses revealed that patients with a foreign body left during a procedure had statistically significant longer mean LOS and greater mean total hospital charges. One can speculate that patients who experienced a foreign body left during a procedure required an additional procedure that translated into a longer hospital stay at greater cost. Interestingly, however, there was no statistically significant difference in mortality between patients with PDI 3 and patients without PDI 3. This finding was in contrast to our previous analysis of the outcomes and risk factors associated with PDI 1, where we found that patients with PDI 1 had statistically significant higher rates of inpatient mortality (as well as longer LOS and greater total hospital charges) compared with patients without PDI 1.15 This can be an important distinction relevant to medicolegal considerations.
The PDIs were developed recently, in 2006,7 and as a result, there are not many studies with which to compare our results. One of the most significant studies that has evaluated the PDIs was published by Scanlon et al7 in 2008. This study recognized the value of PDIs as a potential screening tool to prompt additional medical record review but was critical of the PDI relating to postoperative respiratory failure. Specifically for PDI 3, Scanlon et al reported a rate of 0.09 event per 1000 pediatric discharges. In our analysis, we found the overall rate of PDI 3 in the unmatched data set to be 0.18 case per 1000 pediatric surgical patient discharges. Although higher than the rate reported by Scanlon et al, our analysis was restricted to pediatric surgical patients. Thus, it is not unexpected that this population would have a higher rate of foreign bodies left during a procedure than the general pediatric inpatient population.
Limitations of our analysis include that large administrative databases such as the NIS and KID are limited in the amount of clinical data that can be obtained. Moreover, the accurate identification of PDIs is dependent on the accuracy and completeness of coding of procedures and diagnoses. Also, coding for PDI 3 is not linked to a specific procedure; both codes are recorded in the databases within the same hospitalization record but the temporal relationship of events is not available. The inferences about the associations between PDI 3 and procedure categories must be interpreted with this caution.
A second limitation of this analysis was that procedures were classified into 15 broad categories, with each category consisting of a mix of procedures with varying complexity. It is possible that the associations identified between PDI 3 and procedure category could be biased by the proportion of complex cases within each category, as one would expect that patients undergoing more complex procedures would be at higher risk for a foreign body left during a procedure. One way to avoid this limitation would be to analyze each DRG procedure code individually, but the large numbers of DRG procedure codes would make this analysis challenging.
A final limitation of this analysis was that the case-control matching program used did not create a stratum variable corresponding to each case with its 3 matched controls and therefore we were unable to perform a conditional logistic regression analysis. A matched case-control study with many strata ideally should be analyzed with a conditional logistic regression model since ordinary logistic regression can lead to bias. Rather than perform a true matched case-control analysis, we instead chose to use the matching of cases and controls to create a cohort containing all of the PDI 3 cases and a smaller subset of appropriate controls.
In our analysis, we used a tool developed by AHRQ to identify foreign body left during procedure events (PDI 3) and found them to occur with highest likelihood during gynecologic procedures. This finding has significant cost and morbidity implications because patients with PDI 3 were shown to have a longer LOS and greater total hospital charges compared with patients without PDI 3. Mortality, however, did not differ between the 2 groups. Areas for future study include identifying the specific procedures that are associated with a higher likelihood of PDI 3. This will have implications for providers performing these procedures, because awareness of a higher risk could prompt the need for greater attention to prevent the occurrence of an adverse event. Moreover, the results of this analysis are relevant to the ongoing and evolving process of implementing standardized national outcomes measures for pediatric surgical operations.
Correspondence: Fizan Abdullah, MD, PhD, Johns Hopkins University School of Medicine, 600 N Wolfe St, Harvey 319, Baltimore, MD 21287-0005 (email@example.com).
Accepted for Publication: September 2, 2009.
Author Contributions:Study concept and design: Abdullah and Chrouser. Acquisition of data: Zhang. Analysis and interpretation of data: Camp, Chang, Zhang, Chrouser, Colombani, and Abdullah. Drafting of the manuscript: Camp, Chang, and Abdullah. Critical revision of the manuscript for important intellectual content: Camp, Chang, Zhang, Chrouser, Colombani, and Abdullah. Statistical analysis: Chang and Zhang. Obtained funding: Abdullah. Administrative, technical, and material support: Abdullah. Study supervision: Chrouser and Abdullah.
Financial Disclosure: None reported.
Funding/Support: We thank the Robert Garrett Fund for Treatment of Children, which helped support this study.