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Geospatial Inefficiencies Associated With Digital Replantations at High-Volume Centers and Optimal Allocation Model for Centralization of Replantations
Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OHNational Clinician Scholars Program at the Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI
Corresponding author: Amy M. Moore, MD, Department of Plastic and Reconstructive Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, 915 Olentangy River Road Suite 2100, Columbus, OH 43212.
Digit replantation can improve dexterity, functionality, patient satisfaction, and pain following amputation, but rates continue to fall nationally. This study aimed to describe the effects of travel time and distance as barriers to high-volume hospitals, identify geospatial inefficiencies in the presentation of patients to replantation care, and provide an optimal allocation model in which cases are redistributed to select centers to reduce geospatial redundancies and optimize outcomes.
Methods
We reviewed the California Office of Statewide Health Planning and Development hospital discharge database to identify cases of digital amputation and determine outcomes of replantation. Using residential zip codes, risk- and reliability-adjusted multivariable logistic regression was used to assess the relationship of hospital volume and travel time on replantation success. Geospatial analysis assessed the travel burden of patients as they presented for care, and optimal allocation modeling was used to create a model of centralization.
Results
We identified 5,503 patients during the study period; 1,060 underwent replantation with an overall success rate of 70.2%. Ninety-three hospitals were found to perform replantations, of which only 4 were identified as high-volume hospitals. Patients routinely traveled farther to reach high-volume hospitals, and decreasing the travel time predicted a 15% increase in odds of replantation at a low-volume center. Twenty-one percent of patients presented to a low-volume hospital when a high-volume hospital was closer, and differencein payer type and race/ethnicity existed between those who presented to the closest center compared to those who bypassed high-volume centers. The optimal allocation modeling allocated all cases into 8 centers, which increased the median annual volume from 1 case to 9.6 cases and decreased patient travel time.
Conclusions
Travel burden and geospatial inefficiencies serve as barriers to high-quality and high-volume replantation services. Optimized allocation of digital replantation cases into high-quality centers can decrease travel times, increase annual volumes, and potentially improve replantation outcomes.
Listen to Dr. Graham interview with Drs. Andrew O'Brien and Amy Moore about their article “Geospatial Inefficiencies Associated With Digital Replantations at High-Volume Centers and OptimalAllocation Model for Centralization of Replantations", which is the lead article in this month's issue of the Journal of Hand Surgery.
In some cases, digit replantation may result in lesser pain, higher rates of satisfaction, and improved functionality compared to revision amputations.
Patient-reported and functional outcomes after revision amputation and replantation of digit amputations: the FRANCHISE multicenter international retrospective cohort study.
Finger replantation and amputation challenges in assessing impairment, satisfaction, and effectiveness (FRANCHISE) group. cost-effectiveness of finger replantation compared with revision amputation.
In response, the American College of Surgeons mandated that all level 1 trauma centers provide access to microsurgical services and a formalized plan for replantation care.
Despite this policy, rates of digital replantation are in decline nationally, and replantation services continue to trend toward decentralization and the dispersal of these cases to low-volume centers. High-volume hospitals have not only been shown to have more replantation attempts, but they have also demonstrated a 20% increase in successful replantation outcomes. Yet, more than half of all hospitals performing replantation perform only 1 per year.
In response, the American College of Surgeons and the American Society for Surgery of the Hand have developed the Hand Trauma Center Network: 93 select hospitals committed to serve as referral centers for tertiary hand trauma and microsurgical upper-extremity care, stimulating the need for a data-driven approach to centralization.
This study aimed to describe the effects of travel time and distance as barriers to high-volume hospitals, identify geospatial inefficiencies in the presentation of patients to replantation care, and provide an optimal allocation model in which cases are redistributed to select centers to reduce geospatial redundancies and optimize outcomes.
Materials and Methods
Data source
We reviewed discharge data in the California Office of Statewide Health Planning and Development (OSHPD) hospital discharge database from 2005 to 2016. The OSHPD operated under the authority of the California Health and Human Services Agency to collect and disseminate healthcare discharge data from all practitioners and hospitals within the state. From this data, we identified all cases, variables of interest, and hospitals performing digital replantation; patients were de-identified with encrypted ID assignments. Patient characteristics included age, sex, race/ethnicity, insurance provider, comorbidities, primary residence zip code, and finger affected (thumb vs finger). Hospital characteristics were obtained from the American Hospital Association and merged with data from the OSHPD, including (as a proxy for hospital size) number of beds and operating rooms (ORs), annual case volume, and the presence of resident trainees at any given institution (indicating academic medical centers).
Study cohort
This study included all patients from the database who were 18 years of age or older, resided within the state of California, had been diagnosed with a traumatic finger amputation of a single digit, and presented to a hospital that had ever performed a replantation within the study period. The International Classification of Diseases, Ninth and Tenth revisions (ICD-9/ICD-10) diagnosis and procedure codes were used to define the population of interest (For ICD codes, see Appendix A, available online on the Journal’s website at www.jhandsurg.org). Cases of multi-digit amputations were excluded due to the limitations of the ICD-9/ICD-10 coding system to discern the respective procedure and outcome to the digit of interest. Replantation attempts were defined as patients with ICD-9/ICD-10 procedure codes for replantation; otherwise, patients were considered to have undergone revision amputation. The ICD procedure codes were standardized codes that were reported at the hospital system-level and used to assign Medicare Severity Diagnosis Related Groups to every hospital encounter. Successful replantation was defined as those patients who underwent a replantation attempt without a subsequent amputation, revision, or debridement (identified by the ICD-9/ICD-10 procedure codes) during the same hospitalization. Conversely, failed replantation was defined as those with digital replantation attempts and a subsequent procedure code for amputation, finger debridement, or revision during the same admission. Patient-specific variables selected for analysis included age, race/ethnicity, sex, and insurance type. Additional outcomes of interest were total real driving distance/time traveled from primary residence to destination hospital, as well as the proportion of patients who bypassed a high-volume hospital to ultimately be treated at a low-volume center.
The study was approved by The Ohio State University Wexner Medical Center Institutional Review Board and the California Committee for the Protection of Human Subjects, which waived informed patient consent for deidentified data.
Geospatial analysis
Utilization of location-allocation techniques is emerging as a method to assist in the development of a strategic plan to optimize the delivery of health care services within a network or region. Operations systems engineers have used location-allocation models to determine the optimal location for various medical services based on modifiable criteria. These criteria included considerations such as minimization of total travel time for the entire patient population or maximization of the proportion of patients within a desired travel radius. Similar methodology has been applied to assess the volume-outcome relationship in pancreatic surgery.
For the purpose of this study, we used the Network Analyst Location-Allocation function in ArcGIS Desktop version 10.6 software (ESRI) to generate the geospatial model. The model was optimized to maximize attendance with the minimum number of facilities, while not exceeding facility capacity, assuming only hospitals that performed digital replantation in 2016 existed. Hospital capacity was set to each facility’s historical yearly maximum volume to not exceed realistic capacity at any hospital. This maximum capacity was likely a conservative estimate because our analytic cohort was limited to patients who underwent only single-digit replantation. Based on historical data, single-digit replantations represented 62% to 69% of a given facility’s replantation volume. As such, it was reasonable to infer that most facilities had capacity beyond our restrictions.
Travel distance and time from each patient’s residential zip code to each corresponding medical center were calculated using OpenStreetMap road and traffic data and the OSM2PO routing engine. Patients were identified to be bypassing a high-volume center if the actual travel distance surpassed the shortest travel distance to any given high-volume center. The Network Analyst Location-Allocation function in ArcGIS Desktop 10.6 was used to generate the geospatial model.
test and t test for categorical and continuous variables, respectively. Multivariable risk- and reliability-adjusted analyses were used to correlate factors associated with replantation success and hospital volume while controlling for patient demographics, hospital characteristics, payer status, and travel time. From these analyses, a value of annual case volume that resulted in a marginal predicted improvement in replantation success was identified and used to define high-volume (≥20 annual cases) and low-volume (<20 annual cases) centers for subsequent analysis. This threshold was determined from the literature and a sub-analysis in which we identified a decrease in incremental predicted improvement of successful replantation rate. Increasing annual volume from 15 to 20 cases resulting in an increase in predicted replant success rate by 2.7%; whereas, this rate increased by only 1.5% if annual volume increased from 20 cases to 25.
Because of the marginal improvement predicted between these 2 intervals, 20 annual cases was determined to be an appropriate threshold. Further multivariable logistic regression adjusted for patient and hospital characteristics to assess the relationship of hospital volume and travel time and distance on high-volume or low-volume hospital care. All tests were 2-sided, and P values of less than .05 were considered to indicate statistical significance.
Results
The inclusion criteria were met by 5,503 patients who were diagnosed with a traumatic single-digit amputation and presented to a hospital performing replantation during the investigated time period. Of those, 1,060 (19.4%) underwent a replant attempt within the study period with a mean of 96.4 (SD, 30.1) cases occurring annually (Table 1). The mean age was 42.5 years (SD, 14.5), and 91.3% were male. Nearly half (47.7%) of the cohort was White, followed by Hispanic (39.5%), Asian (6%), and Black (2.2%). The largest insurer of the cohort was workers’ compensation (31.1%), followed by private insurance (30.7%), Medicaid (13.5%), and Medicare (7.2%). Ninety-three hospitals were found to perform digital replantations within the time period, with a mean of 26 (SD, 6.2) hospitals performing replantations each year. The median number of hospital beds and ORs was 314.5 (interquartile range [IQR], 192–408) and 13 (IQR, 8–24), respectively, and 60% of these centers were determined to be academic medical centers with resident trainees. The median travel time to the presenting hospital for all patients undergoing digital replantation was 33.3 (IQR, 15.6–93.8) minutes, and the median travel distance was 28.96 (IQR, 11.5–91.5) miles.
Table 1Characteristics of Patients Undergoing Digital Replantation, 2005–2016
Of the 93 hospitals performing digital replantation from 2006 to 2016, 89 (96%) were low-volume centers (<20 cases/year) and 4 (4%) were found to be high-volume centers. The median annual case volume was 1 (IQR, 1–2) with most centers performing 2 or fewer replantations per year (Table 2). Within the cohort, 4,192 (76.2%) patients received care at low-volume hospitals. Of those, 662 (15.8%) patients underwent a replantation attempt with an unadjusted success rate of 68.1% (Table E1, available online on the Journal’s website at www.jhandsurg.org). Conversely, only 1,311 (23.8%) patients presented to a high-volume hospital, and 398 (30.4%) underwent a replantation attempts, ultimately with an unadjusted success rate of 73.6% (Table E2, available online on the Journal’s website at www.jhandsurg.org).
Table 2Characteristics of Hospitals Performing Digital Replantation, 2006–2016
Hospital Characteristics
Total
N = 93
Annual hospital replantation volume (median, IQR)
1 (1–2)
Annual number of hospitals performing replantation (mean, SD)
When reliability-adjusted and risk-adjusted for both patient and hospital characteristics, high-volume hospitals were shown to be 25.7% more likely to attempt replantation (95% CI, 22.9–28.6; P < .05) than low-volume counterparts. Furthermore, high-volume hospitals demonstrated an 11% greater adjusted replant success rate (95% CI, 5.1–16.9; P < .05) than low-volume hospitals (Table 3). Having Medicaid and workers’ compensation as payer types also predicted worse replantation outcomes (Table 3).
Table 3Predicted Replantation Success Rate by Hospital Volume Adjusted for Patient and Hospital Characteristics
Model adjusted for age, sex, race/ethnicity, discharge year, insurance payer type, comorbidities, finger affected, academic medical centers, number of hospital beds.
Model adjusted for age, sex, race/ethnicity, discharge year, insurance payer type, comorbidities, finger affected, academic medical centers, number of hospital beds.
Model adjusted for age, sex, race/ethnicity, discharge year, insurance payer type, comorbidities, finger affected, academic medical centers, number of hospital beds.
∗ Model adjusted for age, sex, race/ethnicity, discharge year, insurance payer type, comorbidities, finger affected, academic medical centers, number of hospital beds.
Effect of travel time on hospital admission and replantation outcomes
Median travel time and distance for patients undergoing a replant attempt was 22.1 (IQR, 11.8–44.2) minutes and 18.3 (IQR, 8.3–39.9) miles, respectively, if replanted at a low-volume hospital, compared to 92.8 (IQR, 38.2–165.1) minutes and 90.5 (IQR, 31.8– 62.3) miles, respectively, if at a high-volume hospital (P < .05 and P < .05, respectively). In a multivariable, risk-adjusted model, the addition of travel time did not significantly predict changes in replantation success rates, and high-volume hospitals continued to demonstrate a statistically significant increase in predicted replantation success rate (+11.2%; 95% CI 4.8–17.7; P < .05) over low-volume hospitals (Table 3). To account for injuries that occurred at work, presumably some distance from a patient’s primary residence, a sensitivity sub-analysis was performed excluding those with workers’ compensation payer type. This also demonstrated minimal effects of travel time on replantation success by hospital volume (Table 3). On multivariable analysis, each 10-minute decrease in travel time was associated with 15% higher odds of undergoing surgery at a low-volume center (Table 4).
Table 4Multivariable Analysis of Travel Time, Patient Characteristics, and Hospital Characteristics as Predictors for Replantation at a Low-Volume Center
Of the 662 patients who underwent replantation at a low-volume center, 142 (21.45%) were found to have bypassed a closer high-volume center. These patients traveled a median distance of 49.6 (IQR, 26.6–114.7) miles and a median time of 53.2 (IQR, 31.3–109.5) minutes (Table 5). Compared to those patients for which a low-volume center was the closest center, bypassing patients were mostly White (51.4% vs 39.6%; P < .05) and represented a larger proportion of privately-insured patients (43.7% vs 26.7%; P < .05). There were no statistical differences with respect to age, sex, type of finger amputated, or replantation success (Table 5).
Table 5Unadjusted Comparison of Patient Characteristics Presenting to Low-Volume Hospitals by Hospital Bypass Status
Optimal local-allocation model for centralization of digital replantation
The optimal location-allocation model identified 8 hospitals across which to distribute all replantation cases each year; this represented a 91% decrease in the number of hospitals required to perform the annual volume of replantation cases. Furthermore, median annual hospital volume was predicted to be 9.6 (IQR, 4.8–15.9) cases with a minimum annual volume predicted of 1 case and a maximum of 20.2 cases. Median travel time and distance predicted by the optimal location-allocation model was 29.3 (IQR, 17.4–50.4) minutes and 25.7 (IQR, 13.5–47.5) miles, respectively, and both measures were statistically less than that observed in the study period (P < .05 and P < .05, respectively). Figure 1 demonstrates a visual representation of the consolidation of all identified replantations into the 8 designated hospitals and the predicted catchment optimized by travel distance.
Figure 1Gray dots represent individual cases of digital replantation, and H icons represent individual hospitals performing digital replantations within the study period. A Visual representation of all hospitals performing single-digit replantation from 2005-2016. Vector rays depict the geospatial relationship between each case and the hospital at which it was performed. Numerous intersecting rays and prolonged travel demonstrate geospatial inefficiencies. B Optimized consolidation of cases into selected centers to maximize annual volume and minimize patient travel, while not exceeding facility capacities/volumes as demonstrated in the dataset.
Our study corroborated these previous findings on the volume-outcome relationship; high-volume centers had notably higher replant success rates compared with low-volume centers. Yet, low-volume centers were the predominant provider of replantation, performing 1.6 times as many replantation procedures in 22 times as many hospitals. Additionally, we identified travel burden and geospatial inefficiencies as barriers to quality, high-volume care for replantation patients. From this data, we used optimal allocation-modeling (OAM) to demonstrate a model of centralization that minimized geographic redundancies and maximized annual volume to improve acute replantation outcomes.
Travel time and distance influenced the institution at which patients ultimately received replantation care. We found significantly longer travel times were necessary for patients to reach high-volume hospitals. However, differences in travel time did not significantly affect early replantation outcomes; the high- or low-volume status of the hospital did. When considering a model of centralization, longer travel times alone should not preclude presentation to a high-volume hospital, which is also consistent with evidence on prolonged digit viability with cold ischemia time.
Yet, travel times remained predictive of whether or not patients received care from a high-volume or low-volume hospital. In other words, having a low-volume hospital in close proximity to a patient increased the odds of that patient receiving care at that hospital; thus, travel distance and time posed a barrier to receiving high-volume care. Additionally, regression analysis identified Black minority groups, finger versus thumb amputation, and decreased travel time as predictive of receiving care at low-volume centers. As others have reported, the ability for patients to undertake a greater travel burden for high-volume care may be influenced by surgical complexity, existing referral patterns, reimbursement trends, provider preferences, or socioeconomic determinants of patient travel.
In investigating geospatial redundancies, a considerable proportion of patients (21.5%) received care at a low-volume hospital when a closer high-volume hospital existed, representing a possible shortcoming in access to replantation care. We identified unadjusted statistical differences in race/ethnicity and payer type between those who bypassed a high-volume hospital and those who presented to the closest low-volume hospital. A greater proportion of White and privately-insured patients were observed to bypass a high-volume hospital and be admitted to a low-volume hospital. This could represent selection bias in determining referral patterns and supported the need for a more centralized and equitable approach to the care of patients with amputations.
With the increased interest in centralization as an approach to improve outcomes in digital replantation, we used the OAM to propose a consolidation of cases into select higher-volume centers.
The model was able to demonstrate a decrease in travel time, a reduction in the number of centers required to provide for all observed cases, and an increase in the annual volume per hospital. Furthermore, cases were allocated to hospitals without exceeding proven annual volumes, illustrating how redundancies and inefficiencies in the current system of replantation care could be reduced without overburdening selected hospitals. The OAM identified that 8 hospitals could support all digital replantation procedures across the state annually, compared to the observed annual mean of 26 hospitals. Despite the optimal allocation of patients to select hospitals, annual volume for many centers still fell below the high-volume threshold of 20 cases per year. One hospital was predicted by the OAM to perform 1 replantation annually to cover the sparse incidence in Northern California. This finding was likely attributable to the low incidence of the single-digit replantations with an average of 92 cases occurring annually over the study period. However, it was important to note that multivariable risk- and reliability-adjusted analysis estimated a 7.5% increase in replantation success rate at the predicted annual volume. Furthermore, we excluded multi-digit amputations, and the actual number of replantation procedures performed each year was expected to be more than the number identified by this study. The OAM was intended to highlight geospatial inefficiencies and inequities in the travel burden that currently existed in the delivery of replantation care, rather than a concrete prescription of centralization. While not exact, the concentration of replantation services into fewer select hospitals could persuade further allocation of faculty, educational opportunities, and consumable resources into these centers.
While our study provides perspective on the geospatial characteristics of digital replantation care, numerous additional factors, such as provider willingness, emergency response protocols, and patient preference, may also influence the location and number of centers performing digital replantation. A recent study by Cho et al
demonstrated a national trend of single-digit replantation occurring increasingly at large, urban, tertiary centers. Yet, there was a continued downward trend in replantation attempts, suggesting that forces other than centralization were driving the decrease in replantation attempts.
Similarly, our study identified certain minority groups and payer types to be associated with replant success rates, receiving care at low-volume centers, and even bypassing high-volume centers to ultimately receive care at low-volume centers, which underscored the socioeconomic inequities that have perpetuated geospatial barriers to care. Additionally, surgeon-related considerations, such as confidence in microsurgical skills, busy elective schedules, poor compensation, and dissatisfaction with replantation results, have also been cited as limiting factors to performing replantation and likely influence the transfer of patients to and from hospitals.
To improve access and outcomes in digital replantation, each of these elements must ultimately be addressed, which could foreseeably be undertaken within a system of consolidated and centralized services.
Our study has limitations. The scope of the administrative registry limits data to individual hospitalizations and is unable to capture replantation patients who undergo amputation following discharge; this may represent a population who undergo successful revascularization but ultimately result in poor functional outcomes or revision amputation following discharge. Additionally, due to the limitation of ICD-9/ICD-10 coding, multiple-digit amputations are excluded, which is an indication for replantation; thus, we have a conservative estimate of true replantation rates, hospital volume, and reallocations. Patient selection is limited to single-digit replantations to attribute volumes directly to patient outcomes, which is consistent with previously published methodology.
Furthermore, the calculation of travel time and distance is reliant on the zip code of patients’ primary residences, not necessarily where the injuries occur; however, a previous national study revealed that 65% of amputations occurred at home, and the proportion of work-related amputations was decreasing over the study period nationally.
This previous finding correlates with our data, in which 31% of patients are injured at work. Furthermore, our study demonstrates little change in travel times and replantation success rates after excluding patients who would have likely suffered an occupational injury outside of the home, supporting the geospatial inferences of our study. Also, ICD-9/ICD-10 does not provide clinical information, such as mechanism of injury or level of amputation, which would provide greater clarity into indications for replantation. Finally, the findings of the OAM are specific to California, limiting its generalizability; however, the utility of this model is not necessarily in the proposition of specific centers for digital replantation, but to more concretely conceptualize the centralization of digital replantation services and contrast this scenario with the current distribution of resources.
Travel burden and geospatial inefficiencies serve as barriers to high-quality and high-volume replantation services. Optimized allocation of digital replantation cases into high-quality centers can decrease travel times, increase annual volumes, and possibly improve replantation outcomes. We present an idealized model of centralization to serve as an approach to which we may benchmark our current practices.
Appendix A
ICD-10 Diagnosis Code
•
S68.0 Traumatic metacarpophalangeal amputation of thumb
○
S68.01 Complete traumatic metacarpophalangeal amputation of thumb
▪
S68.011 Complete traumatic metacarpophalangeal amputation of right thumb
▪
S68.012 Complete traumatic metacarpophalangeal amputation of left thumb
▪
S68.019 Complete traumatic metacarpophalangeal amputation of unspecified thumb
○
S68.02 Partial traumatic metacarpophalangeal amputation of thumb
▪
S68.021 Partial traumatic metacarpophalangeal amputation of right thumb
▪
S68.022 Partial traumatic metacarpophalangeal amputation of left thumb
▪
S68.029 Partial traumatic metacarpophalangeal amputation of unspecified thumb
•
S68.1 Traumatic metacarpophalangeal amputation of other and unspecified finger
○
S68.11 Complete traumatic metacarpophalangeal amputation of other and unspecified finger
▪
S68.110 Complete traumatic metacarpophalangeal amputation of right index finger
▪
S68.111 Complete traumatic metacarpophalangeal amputation of left index finger
▪
S68.112 Complete traumatic metacarpophalangeal amputation of right middle finger
▪
S68.113 Complete traumatic metacarpophalangeal amputation of left middle finger
▪
S68.114 Complete traumatic metacarpophalangeal amputation of right ring finger
▪
S68.115 Complete traumatic metacarpophalangeal amputation of left ring finger
▪
S68.116 Complete traumatic metacarpophalangeal amputation of right little finger
▪
S68.117 Complete traumatic metacarpophalangeal amputation of left little finger
▪
S68.118 Complete traumatic metacarpophalangeal amputation of other finger
▪
S68.119 Complete traumatic metacarpophalangeal amputation of unspecified finger
○
S68.12 Partial traumatic metacarpophalangeal amputation of other and unspecified finger
▪
S68.120 Partial traumatic metacarpophalangeal amputation of right index finger
▪
S68.121 Partial traumatic metacarpophalangeal amputation of left index finger
▪
S68.122 Partial traumatic metacarpophalangeal amputation of right middle finger
▪
S68.123 Partial traumatic metacarpophalangeal amputation of left middle finger
▪
S68.124 Partial traumatic metacarpophalangeal amputation of right ring finger
▪
S68.125 Partial traumatic metacarpophalangeal amputation of left ring finger
▪
S68.126 Partial traumatic metacarpophalangeal amputation of right little finger
▪
S68.127 Partial traumatic metacarpophalangeal amputation of left little finger
▪
S68.128 Partial traumatic metacarpophalangeal amputation of other finger
▪
S68.129 Partial traumatic metacarpophalangeal amputation of unspecified finger
•
S68.5 Traumatic transphalangeal amputation of thumb
○
S68.51 Complete traumatic transphalangeal amputation of thumb
▪
S68.511 Complete traumatic transphalangeal amputation of right thumb
▪
S68.512 Complete traumatic transphalangeal amputation of left thumb
▪
S68.519 Complete traumatic transphalangeal amputation of unspecified thumb
○
S68.52 Partial traumatic transphalangeal amputation of thumb
▪
S68.521 Partial traumatic transphalangeal amputation of right thumb
▪
S68.522 Partial traumatic transphalangeal amputation of left thumb
▪
S68.529 Partial traumatic transphalangeal amputation of unspecified thumb
•
S68.6 Traumatic transphalangeal amputation of other and unspecified finger
○
S68.61 Complete traumatic transphalangeal amputation of other and unspecified finger(s)
▪
S68.610 Complete traumatic transphalangeal amputation of right index finger
▪
S68.611 Complete traumatic transphalangeal amputation of left index finger
▪
S68.612 Complete traumatic transphalangeal amputation of right middle finger
▪
S68.613 Complete traumatic transphalangeal amputation of left middle finger
▪
S68.614 Complete traumatic transphalangeal amputation of right ring finger
▪
S68.615 Complete traumatic transphalangeal amputation of left ring finger
▪
S68.616 Complete traumatic transphalangeal amputation of right little finger
▪
S68.617 Complete traumatic transphalangeal amputation of left little finger
▪
S68.618 Complete traumatic transphalangeal amputation of other finger
▪
S68.619 Complete traumatic transphalangeal amputation of unspecified finger
○
S68.62 Partial traumatic transphalangeal amputation of other and unspecified finger
▪
S68.620 Partial traumatic transphalangeal amputation of right index finger
▪
S68.621 Partial traumatic transphalangeal amputation of left index finger
▪
S68.622 Partial traumatic transphalangeal amputation of right middle finger
▪
S68.623 Partial traumatic transphalangeal amputation of left middle finger
▪
S68.624 Partial traumatic transphalangeal amputation of right ring finger
▪
S68.625 Partial traumatic transphalangeal amputation of left ring finger
▪
S68.626 Partial traumatic transphalangeal amputation of right little finger
▪
S68.627 Partial traumatic transphalangeal amputation of left little finger
▪
S68.628 Partial traumatic transphalangeal amputation of other finger
▪
S68.629 Partial traumatic transphalangeal amputation of unspecified finger
ICD-10 Procedure Code
•
0XML Thumb, Right
○
0XML0ZZ Reattachment of Right Thumb, Open Approach
•
0XMM Thumb, Left
○
0XMM0ZZ Reattachment of Left Thumb, Open Approach
•
0XMN Index Finger, Right
○
0XMN0ZZ Reattachment of Right Index Finger, Open Approach
•
0XMP Index Finger, Left
○
0XMP0ZZ Reattachment of Left Index Finger, Open Approach
•
0XMQ Middle Finger, Right
○
0XMQ0ZZ Reattachment of Right Middle Finger, Open Approach
•
0XMR Middle Finger, Left
○
0XMR0ZZ Reattachment of Left Middle Finger, Open Approach
•
0XMS Ring Finger, Right
○
0XMS0ZZ Reattachment of Right Ring Finger, Open Approach
•
0XMT Ring Finger, Left
○
0XMT0ZZ Reattachment of Left Ring Finger, Open Approach
•
0XMV Little Finger, Right
○
0XMV0ZZ Reattachment of Right Little Finger, Open Approach
•
0XMW Little Finger, Left
○
0XMW0ZZ Reattachment of Left Little Finger, Open Approach
•
0X6L Thumb, Right
○
0X6L0Z0 Detachment at Right Thumb, Complete, Open Approach
○
0X6L0Z1 Detachment at Right Thumb, High, Open Approach
○
0X6L0Z2 Detachment at Right Thumb, Mid, Open Approach
○
0X6L0Z3 Detachment at Right Thumb, Low, Open Approach
•
0X6M Thumb, Left
○
0X6M0Z0 Detachment at Left Thumb, Complete, Open Approach
○
0X6M0Z1 Detachment at Left Thumb, High, Open Approach
○
0X6M0Z2 Detachment at Left Thumb, Mid, Open Approach
○
0X6M0Z3 Detachment at Left Thumb, Low, Open Approach
•
0X6N Index Finger, Right
○
0X6N0Z0 Detachment at Right Index Finger, Complete, Open Approach
○
0X6N0Z1 Detachment at Right Index Finger, High, Open Approach
○
0X6N0Z2 Detachment at Right Index Finger, Mid, Open Approach
○
0X6N0Z3 Detachment at Right Index Finger, Low, Open Approach
•
0X6P Index Finger, Left
○
0X6P0Z0 Detachment at Left Index Finger, Complete, Open Approach
○
0X6P0Z1 Detachment at Left Index Finger, High, Open Approach
○
0X6P0Z2 Detachment at Left Index Finger, Mid, Open Approach
○
0X6P0Z3 Detachment at Left Index Finger, Low, Open Approach
•
0X6Q Middle Finger, Right
○
0X6Q0Z0 Detachment at Right Middle Finger, Complete, Open Approach
○
0X6Q0Z1 Detachment at Right Middle Finger, High, Open Approach
○
0X6Q0Z2 Detachment at Right Middle Finger, Mid, Open Approach
○
0X6Q0Z3 Detachment at Right Middle Finger, Low, Open Approach
•
0X6R Middle Finger, Left
○
0X6R0Z0 Detachment at Left Middle Finger, Complete, Open Approach
○
0X6R0Z1 Detachment at Left Middle Finger, High, Open Approach
○
0X6R0Z2 Detachment at Left Middle Finger, Mid, Open Approach
○
0X6R0Z3 Detachment at Left Middle Finger, Low, Open Approach
•
0X6S Ring Finger, Right
○
0X6S0Z0 Detachment at Right Ring Finger, Complete, Open Approach
○
0X6S0Z1 Detachment at Right Ring Finger, High, Open Approach
○
0X6S0Z2 Detachment at Right Ring Finger, Mid, Open Approach
○
0X6S0Z3 Detachment at Right Ring Finger, Low, Open Approach
•
0X6T Ring Finger, Left
○
0X6T0Z0 Detachment at Left Ring Finger, Complete, Open Approach
○
0X6T0Z1 Detachment at Left Ring Finger, High, Open Approach
○
0X6T0Z2 Detachment at Left Ring Finger, Mid, Open Approach
○
0X6T0Z3 Detachment at Left Ring Finger, Low, Open Approach
•
0X6V Little Finger, Right
○
0X6V0Z0 Detachment at Right Little Finger, Complete, Open Approach
○
0X6V0Z1 Detachment at Right Little Finger, High, Open Approach
○
0X6V0Z2 Detachment at Right Little Finger, Mid, Open Approach
○
0X6V0Z3 Detachment at Right Little Finger, Low, Open Approach
•
0X6W Little Finger, Left
○
0X6W0Z0 Detachment at Left Little Finger, Complete, Open Approach
○
0X6W0Z1 Detachment at Left Little Finger, High, Open Approach
○
0X6W0Z2 Detachment at Left Little Finger, Mid, Open Approach
○
0X6W0Z3 Detachment at Left Little Finger, Low, Open Approach
ICD-9 Diagnosis Code
•
885.0 Traumatic thumb amputation
•
885.1 Traumatic thumb amputation with complication
•
886.0 Traumatic finger amputation
•
886.1 Traumatic finger amputation with complication
ICD-9 Procedure Code
•
84.21 Thumb reattachment
•
84.02 Thumb amputation
•
84.22 Finger reattachment
•
84.01 Finger amputation
•
84.3 Revision of amputation stump
•
86.22 Excisional debridement of wound, infection, or burn
Table E1Characteristics of High-Volume and Low-Volume Hospitals
Patient-reported and functional outcomes after revision amputation and replantation of digit amputations: the FRANCHISE multicenter international retrospective cohort study.
Finger replantation and amputation challenges in assessing impairment, satisfaction, and effectiveness (FRANCHISE) group. cost-effectiveness of finger replantation compared with revision amputation.
A. D. received salary support from the Veterans Affairs Office of Academic Affiliations during the time of the study. The rest of the authors declare that no benefits in any form have been received or will be received related directly or indirectly to the subject of this article.
Disclaimer: This does not necessarily represent the views of the United States Government or Department of Veterans Affairs.
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