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PLOS Medicine logoLink to PLOS Medicine
. 2020 Aug 31;17(8):e1003291. doi: 10.1371/journal.pmed.1003291

Factors associated with implant survival following total hip replacement surgery: A registry study of data from the National Joint Registry of England, Wales, Northern Ireland and the Isle of Man

Jonathan Thomas Evans 1,*, Ashley William Blom 1,2, Andrew John Timperley 3,4, Paul Dieppe 5, Matthew James Wilson 3, Adrian Sayers 1,, Michael Richard Whitehouse 1,2,
Editor: Rob GHH Nelissen6
PMCID: PMC7458308  PMID: 32866147

Abstract

Background

Nearly 100,000 people underwent total hip replacement (THR) in the United Kingdom in 2018, and most can expect it to last at least 25 years. However, some THRs fail and require revision surgery, which results in worse outcomes for the patient and is costly to the health service. Variation in the survival of THR implants has been observed between units and reducing this unwarranted variation is one focus of the “Getting it Right First Time” (GIRFT) program in the UK. We aimed to investigate whether the statistically improved implant survival of THRs in a high-performing unit is associated with the implants used or other factors at that unit, such as surgical skill.

Methods and findings

We analyzed a national, mandatory, prospective, cohort study (National Joint Registry for England, Wales, Northern Ireland and the Isle of Man [NJR]) of all THRs performed in England and Wales. We included the 664,761 patients with records in the NJR who have received a stemmed primary THR between 1 April 2003 and 31 December 2017 in one of 461 hospitals, with osteoarthritis as the only indication. The exposure was the unit (hospital) in which the THR was implanted. We compared survival of THRs implanted in the “exemplar” unit with THRs implanted anywhere else in the registry. The outcome was revision surgery of any part of the THR construct for any reason. Net failure was calculated using Kaplan–Meier estimates, and adjusted analyses employed flexible parametric survival analysis.

The mean age of patients contributing to our analyses was 69.9 years (SD 10.1), and 61.1% were female. Crude analyses including all THRs demonstrated better implant survival at the exemplar unit with an all-cause construct failure of 1.7% (95% CI 1.3–2.3) compared with 2.9% (95% CI 2.8–3.0) in the rest of the country after 13.9 years (log-rank test P < 0.001). The same was seen in analyses adjusted for age, sex, and American Society of Anesthesiology (ASA) score (difference in restricted mean survival time 0.12 years [95% CI 0.07–0.16; P < 0.001]). Adjusted analyses restricted to the same implants as the exemplar unit show no demonstrable difference in restricted mean survival time between groups after 13.9 years (P = 0.34).

A limitation is that this study is observational and conclusions regarding causality cannot be inferred. Our outcome is revision surgery, and although important, we recognize it is not the only marker of success of a THR.

Conclusions

Our results suggest that the “better than expected” implant survival results of this exemplar center are associated with implant choice. The survival results may be replicated by adopting key treatment decisions, such as implant selection. These decisions are easier to replicate than technical skills or system factors.


In a registry-based prospective study, Jonathan Thomas Evans and colleagues investigate factors associated with implant survival among total hip replacement surgeries conducted at hospitals in the UK.

Author summary

Why was this study done?

  • In general, total hip replacement (THR) is safe and effective at reducing pain and restoring mobility to people with end-stage arthritis of the hip.

  • In England and Wales, in 2017, over 822 different types of hip replacement were used, and different brands of hip replacement have been shown to have varying survival rates at different follow-up timepoints. Reducing variation in outcomes following surgery is an important aim of the National Health Service (NHS) in England and Wales.

  • A national database of all hip replacements in England and Wales (the National Joint Registry) has shown variation in survival rates between different hospitals, and a few hospitals are highlighted by the database for having better survival rates than the others.

What did the researchers do and find?

  • One of the hospitals with better survival rates for hip replacements than the others uses only one type of hip replacement for all patients.

  • We compared the survival of THRs implanted in this one hospital to THRs implanted anywhere else in the country to look for factors that are associated with improved survival.

  • When this hospital was compared with everyone else using the same hip replacement, after taking the patients’ age, sex, and general health into account, they no longer had better results than anyone else.

What do these findings mean?

  • These findings suggest that the better results seen in this one hospital are not associated with the skill of the surgeon or the setup of the hospital but are associated with the choice of hip replacement.

  • Future studies are needed to determine whether this is also the case across other brands of hip replacement and to determine whether the choice of implant is similarly associated with implant survival across other specialties.

Introduction

Total hip replacement (THR) is one of the most successful operations of our time with nearly 100,000 performed in England, Wales, Northern Ireland, and the Isle of Man in 2017 [1, 2]. They have been shown in general to last about 25 years, but despite this, there is still variation in the survival of implants across the UK [2, 3]. THR components may require changing (via revision surgery) for one of several reasons including infection, wear, loosening, fracture, or instability [2]. The need for future revision surgery can be influenced by preoperative patient factors, implant factors, and surgical factors [4, 5]. It has previously been demonstrated that THR revisions are not as effective in improving pain and function as the primary operation, have a high chance of further revision, and are costly to the health service, as well as resulting in exposure of patients to the additional pain and inconvenience of another operation [2, 6, 7]. Although implant survival is not the only marker of success [8], the cumulative probability of revision of THRs is a readily available outcome measure because of the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man (NJR) a mandatory, national database [9].

The NJR has collected data since 2003 and at the time of writing contains in excess of 1 million records of primary THRs. In 2017, the NJR identified at least 415 different units (hospitals) performing THRs using at least 822 different combinations of femoral stem and acetabular socket [10, 11] and is thought to capture over 95% of primary hip and knee operations and 90% of revisions [2]. Every year, the NJR annual report lists units in which either a higher or lower than expected rate of revision has been observed over the preceding years. The units with a “better than expected” (above the 99.7% confidence limit) revision estimate may offer an opportunity to learn from good practice and potentially reduce variation between units.

The importance of reducing unwarranted variation across the whole National Health Service (NHS) has been highlighted by the recent work of the “Getting it Right First Time” (GIRFT) program [12]. This review has previously highlighted variation in adult elective orthopedic services and makes clear the requirement to learn from good performance [13].

Investigating what may lead one unit to demonstrate better results than others is challenging because of the differing patient populations as well as issues with potential selection bias. Patients may receive different types of implants based on factors such as age and sex, and as a result, outcomes are difficult to interpret. One unit in the NJR, the Royal Devon & Exeter NHS Foundation Trust (RD&E), has been repeatedly identified as having “better than expected” survival outcomes and is widely known for using only one femoral stem (the Exeter V40 femoral stem) in all routine primary THRs regardless of patient factors, thus removing or reducing selection bias [2]. This offers an opportunity to investigate whether “better than expected” survival results observed within this unit were due to a unit effect or because of the implants used.

Methods

We aimed to compare the cumulative revision estimates between the RD&E and the rest of the country to investigate whether “better than expected” outcomes were due to the implants used or because of other unit factors.

The NJR is a mandatory national audit of joint replacement activity. After gaining written consent, operations are reported to the NJR by the healthcare provider at the time of surgery. The dataset consisted of 981,269 linked primary THRs performed in England and Wales between 1 April 2003 and 31 December 2017 with consent for data linkage. Data were censored either by death or administratively on 31 December 2017. After exclusion of THRs with incomplete or inconsistent data or using metal-on-metal bearings, we were left with 664,761 primary THRs, in which osteoarthritis was the only indication for THR. Reasons for exclusion at each stage are shown in Fig 1.

Fig 1. Reasons for exclusion from analyses.

Fig 1

A sequence is the order of operations recorded in the NJR for any patient. All complete records will start with a primary operation. If a sequence starts with a revision, the primary was performed before the NJR, outside the geographical coverage of the NJR, or data were not submitted to the NJR. THR, total hip replacement; NJR, National Joint Registry for England, Wales, Northern Ireland and the Isle of Man.

Statistical analysis

Statistical analysis was performed with Stata 15 (Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC, https://www.stata.com/). The exposure of interest was the unit in which the THR was performed, and the 2 groups were THRs performed at the RD&E and THRs performed in any other unit. The choice of the RD&E as the exposure group (rather than any of the other units with “better than expected” survival results) is due to a lack of selection bias, in that every patient in this unit receives the same femoral stem, regardless of age, sex, or indication. This lack of selection bias is unique to this unit.

The outcome of interest was revision of any part of the THR for any reason. All-cause revision was defined using the NJR definition as the addition, removal or modification of any part of the construct [2]. The study population was all THRs implanted in the NJR; subgroup analysis was performed for THRs using any type of cemented stem (hybrid or all-cemented constructs) as well as THRs using the Exeter V40 femoral stem (hybrid or all-cemented constructs), the stem used by the RD&E.

Unadjusted survival estimates were calculated using the Kaplan–Meier (KM) method for all included THRs, stratified by the exposure of interest [14]. Flexible parametric survival analysis (FPSA), as described by Royston and Parmar, was used to look for time varying effects in the 2 exposure groups by plotting time-dependent against proportional hazards models [15]. FPSA models were then used to compare THRs performed at the RD&E to those performed in any other unit, having adjusted for age, sex, and American Society of Anesthesiology (ASA) score at time of surgery and allowing for time varying effects. FPSA models were assessed visually for goodness of fit against KM curves, in THRs using the Exeter V40 femoral stem. FPSA modeling offers an advantage over more traditional semiparametric techniques, such as Cox regression, because in addition to allowing effects to vary with time via cubic splines, they allow us to estimate a baseline hazard function. This baseline hazard allows the estimation of absolute effects, such as survival, for both groups given certain values of covariates, rather than simply an estimate of the relative effect between the 2 groups (hazard ratio) as is given in Cox regression. Graphs comparing revision estimates between the 2 exposure groups were fitted to models for a 68-year-old female patient, to reflect the median age and most common sex receiving primary THRs in the NJR. Restricted mean survival times were calculated using the standardized survival package “stpm2_standsurv” [16].

Data were censored either by death, or administratively on 31 December 2017. THRs with incomplete or inconsistent data or using metal-on-metal bearings (previously shown to demonstrate poorer survival [17]) were excluded, and cases were included only where osteoarthritis was the sole indication for THR. Reasons for exclusion at each stage are shown in Fig 1.

Sensitivity analyses

Other potential confounders were considered with a priori knowledge and focusing on variables that were determined before the choice of implant was made, rather than those potentially related to implant choice and thus potentially mediators (e.g., surgical approach and anesthetic). Socioeconomic status (SES) and body mass index (BMI) may also be important potential confounders. Socioeconomic status was assessed using deciles of the Index of Multiple Deprivation (IMD) organized by Lower Layer Super Output Areas (LSOAs), and BMI was treated as a categorical variable using World Health Organization categories (<18.5, 18.5–24.9, 25–29.9, 30–34.9, 35–39.9, and >40 kg/m2). S1 Table shows the distribution of BMI across the strata for the overall cohort as well as the 2 exposure groups.

Cumulative revision estimates were explored restricting analyses to only THRs using the same implant combinations as the exemplar center. Construct survival of THRs using the 5 most implanted cemented stems were explored to determine whether the similar results could be achieved with other commonly used implants within the same type of construct fixation.

Missing data

Cases missing data on potential confounders (age, sex, ASA score, BMI, or socioeconomic status) were retained in analyses that were not using that specific model as a covariate. A table detailing the distribution of missing data on these covariates can be seen in S2 Table. Data regarding SES were only available for patients operated in England, and BMI was missing in 30.1% of cases. Complete case analysis models including these variables were completed as sensitivity analyses as multiple imputation of these data may introduce bias if they are not missing truly at random [18].

Patient and public involvement

Patients were involved in the design of this study through the Patient Experience in Research (PEPR) group at the Musculoskeletal Research Unit, University of Bristol [19], and in the NJR Research Sub-committee who provided authority for this study. The same groups will be involved in the dissemination of results. The choice of outcome of interest (all-cause revision rather than revision for specific indications) was guided by the PEPR group.

Planning of analyses

The analysis plan was made prior to the start of all analyses and agreed on among co-authors. No data-driven changes to the analysis plan were made.

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 STROBE Checklist). Approval for this study was granted by the NJR Research Sub-committee (reference RSC2017/15). Written consent was granted by patients for inclusion of their data and its use in research within the NJR.

Results

After exclusions, we were left with 664,761 primary THRs for analysis. The maximum follow-up in the exemplar center group was 13.9 years and was 14.2 years in all other units. The demographics and distribution of the THRs in each group can be seen in Table 1. Of the 6,230 cases performed at the RD&E, there were 83 different recorded “lead” surgeons who performed a range from one THR to 992 THRs included in the study dataset. A total of 68.1% of THRs were performed by 1 of 6 surgeons. The “lead” surgeon may be a consultant (attending), fellow, or higher specialist trainee (resident) operating under the supervision of a consultant.

Table 1. Demographics and distribution of included total hip replacements.

All total hip replacements Constructs using a cemented stem Constructs using the same femoral stem as the exemplar center
Operated on at the exemplar center Not operated on at the exemplar center Operated on at the exemplar center Not operated on at the exemplar center Operated on at the exemplar center Not operated on at the exemplar center
Total, n 6,230 658,531 6,228 379,691 6,227 228,814
Female, n (%) 3,621 (58.1) 402,406 (61.1) 3,619 (58.1) 245,891 (64.8) 3,619 (58.1) 146,219 (63.9)
Mean age, SD 70.2 (10.6) 69.9 (10.1) 70.2 (10.6) 72.5 (9.2) 70.2 (10.6) 72.1 (9.3)
Mean body mass index, kg/m2 (SD) 28.6 (5.2) 28.7 (5.2) 28.6 (5.2) 28.4 (5.1) 28.6 (5.2) 28.5 (5.1)
Posterior approach, n (%) 5,553 (89.1) 377,802 (57.4) 5,552 (89.1) 208,652 (55.0) 5,551 (89.1) 136,090 (59.5)
American Society of Anesthesiologists score, n (%) I 963 (15.5) 98,212 (14.9) 963 (15.5) 48,189 (12.7) 963 (15.5) 29,987 (13.1)
II 4,499 (72.2) 462,006 (70.2) 4,497 (72.2) 265,746 (70.0) 4,496 (72.2) 159,682 (69.8)
III 756 (12.1) 95,507 (14.5) 756 (12.1) 63,868 (16.8) 756 (12.1) 38,050 (16.6)
IV & V 12 (0.2) 2,806 (0.4) 12 (0.2) 1,888 (0.5) 12 (0.2) 1,095 (0.5)
National Health Service funded, n (%) 6,123 (98.3) 552,907 (84.0) 6,121 (98.3) 317,950 (83.7) 6,120 (98.3) 193,566 (84.6)
Consultant as operating surgeon, n (%) 3,004 (48.2) 546,315 (83.0) 3,002 (48.2) 302,394 (79.6) 3,001 (48.2) 184,886 (80.8)
Cemented acetabulum, n (%) 4,927 (79.1) 263,055 (39.9) 4,927 (79.1) 244,644 (64.4) 4,926 (79.1) 147,578 (64.5)

Table demonstrating the demographics and distribution of all total hip replacements included in this study broken down by exposure groups and sensitivity analysis subgroups.

Crude analyses

The crude 10-year cumulative revision estimate of all THRs implanted at the RD&E was 1.7% (95% CI 1.3–2.3). In all other units, the 10-year cumulative revision estimate for all THRs was 2.9% (95% CI 2.8–3.0; log-rank test P < 0.001); for just THRs using cemented stems, it was 2.6% (95% CI 2.5–2.7; log-rank test P = 0.007), and for just THRs using the Exeter V40 femoral stem, it was 2.3% (95% CI 2.2–2.4) (log-rank test P = 0.05). Net revision estimates calculated using 1-Kaplan–Meier curves can be seen in Fig 2; the number of hips at risk at all time points for all analyses can be seen in S3 Table.

Fig 2. Unadjusted 1-Kaplan–Meier revision estimates of total hip replacements in each subgroup.

Fig 2

Comparison of the all-cause construct revision estimates of total hip replacements performed in the exemplar center compared with those performed in all other hospitals in the National Joint Registry.

Adjusted analyses

A FPSA model was fitted for all THRs using the Exeter V40 femoral stem and showed excellent “goodness of fit” (S1 Fig). Comparison of time-dependent and proportional hazards models suggested the time-dependent model showed better fit (S2 Fig). After adjustment for age, sex, and ASA and allowing for time varying effects, the relative revision estimates of each subgroup of THRs (THRs using a cemented stem or THRs using the Exeter V40 femoral stem) modeled for a 68-year-old, female patient, are shown in Fig 3A, Fig 3B and Fig 3C.

Fig 3. FPSA adjusted for age, sex, and American Society of Anesthesiology score.

Fig 3

Results presented for a 68-year-old female patient with an American Society of Anesthesiology score of 2. FPSA, flexible parametric survival analysis.

The femoral stem was paired with 9 different acetabular components in the RD&E, and 99% of these THRs used 1 of only 3 cups. In other units, the Exeter V40 femoral stem was paired with 111 different acetabular components. Fig 3D shows a comparison of the 2 groups if analyses are restricted to stem/cup combinations used at the RD&E. This restricted analysis compares 6,227 performed in the RD&E with 148,295 THRs performed elsewhere. After 13.9 years, there is a discrepancy in restricted mean survival time (RMST) of 0.02 years (95% CI −0.02 to 0.07; P = 0.33). A P value of 0.33 suggests there is little or no evidence of any difference in survival of THRs after 13.9 years between those implanted in the RD&E compared with elsewhere when the same implants were used. Fig 4 shows how the difference in RMST between the 2 subgroups changes over time. RMST is reported in years and estimates the difference in life expectancy of the THR between the 2 exposure groups, i.e., the extra time a THR lasts because it was implanted in the RD&E. The difference in RMST changes slightly over time, most notably at 2 time points, 3 years and at 10 years. This may reflect particular modes of failure such as loosening of cups at 10 years, potentially due to cementation technique. It should be noted that for the majority of time reported, the confidence intervals cross the null value of 0. Graphically, it appears that for a short period there may be a transient center effect; however, analysis of the entire period shows no such center effect.

Fig 4. Difference in RMST between total hip replacements performed at the RD&E and in all other hospitals combined when using the same implants as those used at the RD&E.

Fig 4

A demonstration of how the difference in RMST varies between the 2 exposure groups using the flexible parametric model adjusted for age, sex, and American Society of Anesthesiologists score and allowing for time varying effects. RMST, restricted mean survival time; RD&E, Royal Devon & Exeter.

Sensitivity analyses

A complete case analysis including socioeconomic status in the model excludes 16 cases from the RD&E (0.3%) and 8,569 cases performed elsewhere (5.8%). The results of this model are very similar to that described previously (S3 Fig). Complete case analysis with BMI included in the model, again, shows roughly similar results; however, given the much higher proportion of missing data, the CIs are wider (S4 Fig).

Analysis of the all-cause construct survival of THRs using the 5 most commonly implanted cemented stems across the NJR to date, shows that other stems may achieve comparable performance to the Exeter V40 femoral stem, but this is not true of all stems with the same mode of fixation (S5 Fig).

Discussion

After 13.9 years, both crude and adjusted cumulative revision estimates showed better implant survival when THRs were performed at the RD&E compared with elsewhere in the country. In analyses adjusted for age, sex, and ASA score, these differences attenuated after restricting to only cemented implants and disappeared when only THRs using the same implant combinations as the RD&E were analyzed. This suggests that implant choice is responsible for the “better than expected” results at the RD&E and not unit (or surgeon) factors.

We are unaware of any studies to date investigating the reasons why 1 unit achieves better THR survival than others. This study suggests that when attempting to improve implant survivorship, units performing THR, particularly those with “lower than expected” implant survival, should focus attention on choice of implant rather than other factors. The use of implants without evidence of good long-term survival should be limited to well-controlled and monitored studies or experiments. Although this study has focused on 1 single femoral stem (the Exeter V40 femoral stem), we believe that the observed high survival would be reproducible with other well performing implants. Previous work by Deere and colleagues has compared the survival of implant combinations after 10 years and provides a reference to demonstrate other implant combinations with low revision rates [5]. The NJR annual report provides a list of units with “better-than-expected” survival results as well as survival estimates for individual stem/cup combinations and can act as a reference document to units wishing to review their implant selection. These findings are of relevance to surgeons, commissioners, and most importantly, patients when deciding whether to, where, and when to have a THR. Patients should be encouraged to ask surgeons about the long-term survival evidence for the implant they plan to use.

The strength of this study stems from the high number of patients included and the use of a linked, national database with high capture of revision procedures. The lack of selection bias in choice of femoral stem at the RD&E (our reference unit) is another strength. The data in this study are, however, observational, and conclusions regarding causality must be interpreted with caution. Our outcome is revision surgery, and although important, it is not the only marker of success of a THR. Patient reported outcomes such as pain and function have not been assessed and patients may have been unsuitable or unwilling to undergo a revision operation and as such a failure may have been misclassified as a success. We made no attempt to restrict by bearing surfaces in this study, which may be a contributing factor in the longevity of a THR; this would, however, have created several subgroups, which we wished to avoid so we could maintain sample size. We would expect the complexity of cases to be generally representative of the UK population given the NHS referral system and have additionally made attempts to adjust for potential confounders; however, there may still be some residual confounding for variables with incomplete data or not captured by the dataset. There are likely to be sequential hip replacements performed on different sides within the same patient included in this study. We treated each hip as an individual case. There is a risk of failure of one THR leading to subsequent failure of the other side in cases of infection; however, given that this is also the case from other joint replacements (e.g., knee) or from other conditions leading to a higher propensity to infection, we felt this risk was negligible and therefore did not exclude these from analyses. The use of complete case analysis over multiple imputation for handling missing covariates was also a potential weakness and may result in a loss of power by restricting the sample size. Given the distribution of missing data (S1 Table) and the large numbers offered by the registry, we felt that a complete case analysis was suitable for this study, and any reduction in power would be negligible. We cannot exclude the possibility that better surgeons may choose prostheses with lower revision rates.

The fact that the results seen at the RD&E were achieved with 83 different lead surgeons supports the theory that the implant is the driver of improved survival results rather than the skill of the individual surgeon. If this observed association is indeed true, the use of implants with evidence of good survival should be encouraged throughout the health service. Further work may focus on the effect of a change in implant use of a single hospital/unit on survival results in time-series analyses. Although implant selection appears to be associated with improved survival in THR, it is yet to be seen whether the same phenomenon is true in other branches of medicine heavily reliant on implantable devices. Further research in other areas is warranted to investigate this effect.

Conclusion

In this study, we found evidence suggesting that implant selection is associated with the long-term survival of THRs rather than factors specific to a high-performing unit. Surgeons, commissioners, and patients should use this information when considering THR.

Supporting information

S1 STROBE Checklist. Annotated STROBE checklist detailing how this study meets the criteria laid out in the STROBE statement.

STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOC)

S1 Fig. Demonstration of goodness of fit of flexible parametric survival analysis model for total hip replacements using the Exeter V40 femoral stem.

Goodness of fit was assessed visually using the above figure as well as by assessment of log-likelihood of different models. A model with 5 knots was chosen as further knots provided more complexity with little improvement in log-likelihood.

(TIF)

S2 Fig. Demonstration of nonproportionality of hazard of revision.

The above figure demonstrates that when using a flexible parametric survival analysis model that allows the hazard of failure to vary with time to compare TD and PH models. There is an apparent difference between the hazard of failure at the exemplar center and in all other units (the solid lines). The fact that these solid lines cross is highly indicative of the fact that the hazards are not proportional through the entire follow-up of the study. PH, proportional hazard; TD, time dependent.

(TIF)

S3 Fig. FPSA complete case analysis adjusted for age, sex, American Society of Anesthesiology score, and socioeconomic status.

Results presented for a 68-year-old female patient with an American Society of Anesthesiology score of 2 and in the 10th decile of IMD organized by LSOA. FPSA, flexible parametric survival analysis; IMD, Index of Multiple Deprivation; LSOA, Lower Layer Super Output Area.

(TIF)

S4 Fig. FPSA complete case analysis adjusted for age, sex, American Society of Anesthesiology score, socioeconomic status, and body mass index.

Results presented for a 68-year-old female patient with an American Society of Anesthesiology score of 2 and in the 10th decile of IMD organized by LSOA and body mass index in World Health Organization category 2. FPSA, flexible parametric survival analysis; IMD, Index of Multiple Deprivation; LSOA, Lower Layer Super Output Area.

(TIF)

S5 Fig. Comparison of the all-cause construct survival of the 5 most used cemented stems of all time in combination with any cup.

A comparison of the probability of all-cause revision (1 –Kaplan–Meier) for all constructs using the 5 most frequently implanted cemented femoral stems, demonstrating the differences in revision estimates between these stems. This suggests that the results demonstrated in this study may be achievable with other femoral stems.

(TIF)

S1 Table. Distribution of BMI across categories.

A comparison of the distribution of BMI between the 2 exposure categories (Royal Devon & Exeter hospital and all other hospitals combined). BMI, body mass index.

(DOCX)

S2 Table. Distribution of missing data.

Table detailing the distribution of missing data between the exposure categories (Royal Devon & Exeter hospital and all other hospitals combined).

(DOCX)

S3 Table. At-risk table.

Table demonstrating the number of total hip replacements at risk at each time point following operation. For use in the interpretation of previous survival graphs.

(DOCX)

Acknowledgments

We thank the patients and staff of all the hospitals in England, Wales and Northern Ireland who have contributed data to the National Joint Registry.

The Healthcare Quality Improvement Partnership (HQIP) and/or the NJR take no responsibility for the accuracy, currency, reliability, and correctness of any data used or referred to in this report, nor for the accuracy, currency, reliability, and correctness of links or references to other information sources and disclaims all warranties in relation to such data, links, and references to the maximum extent permitted by legislation.

HQIP and NJR shall have no liability (including but not limited to liability by reason of negligence) for any loss, damage, cost or expense incurred or arising by reason of any person using or relying on the data within this report and whether caused by reason of any error, omission, or misrepresentation in the report or otherwise. This report is not to be taken as advice. Third parties using or relying on the data in this report do so at their own risk and will be responsible for making their own assessment and should verify all relevant representations, statements, and information with their own professional advisers.

The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Abbreviations

ASA

American Society of Anesthesiology

FPSA

flexible parametric survival analysis

GIRFT

Getting it Right First Time

HQIP

Healthcare Quality Improvement Partnership

NHS

National Health Service

NJR

National Joint Registry for England, Wales, Northern Ireland and the Isle of Man

RD&E

Royal Devon & Exeter NHS Foundation Trust

RMST

restricted mean survival time

THR

total hip replacement

Data Availability

Data cannot be shared publicly. Data are available from the NJR research subcommittee researchers who meet the criteria for access to confidential data. Access to the data used in this study can be requested via njrresearch@hqip.org.uk. Full details of how to request NJR data for research can be found at: http://www.njrcentre.org.uk/njrcentre/Research/Research-requests.

Funding Statement

This study was supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. JTE was supported by the joint National Joint Registry of England, Wales, Northern Ireland and the Isle of Man and Royal College of Surgeons of England Fellowship. AS was supported by a MRC strategic skills fellowship: MRC Fellowship MR/L01226X/1 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Learmonth ID, Young C, Rorabeck C. The operation of the century: total hip replacement. Lancet. 2007;370(9597):1508–19. 10.1016/S0140-6736(07)60457-7 [DOI] [PubMed] [Google Scholar]
  • 2.National Joint Registry for England, Wales, Northern Ireland and Isle of Man. 15th Annual Report. 2018 Sep.
  • 3.Evans JT, Evans JP, Walker RW, Blom AW, Whitehouse MR, Sayers A. How long does a hip replacement last? A systematic review and meta-analysis of case series and national registry reports with more than 15 years of follow-up. Lancet. 2019;393(10172):647–54. 10.1016/S0140-6736(18)31665-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Bayliss LE, Culliford D, Monk AP, Glyn-Jones S, Prieto-Alhambra D, Judge A, et al. The effect of patient age at intervention on risk of implant revision after total replacement of the hip or knee: a population-based cohort study. Lancet. 2017;389(10077):1424–30. 10.1016/S0140-6736(17)30059-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Deere KC, Whitehouse MR, Porter M, Blom AW, Sayers A. Assessing the non-inferiority of prosthesis constructs used in hip replacement using data from the National Joint Registry of England, Wales, Northern Ireland and the Isle of Man: a benchmarking study. BMJ Open. 2019;9(4):e026685 10.1136/bmjopen-2018-026685 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Lenguerrand E, Whitehouse MR, Wylde V, Gooberman-Hill R, Blom AW. Pain and Function Recovery Trajectories following Revision Hip Arthroplasty: Short-Term Changes and Comparison with Primary Hip Arthroplasty in the ADAPT Cohort Study. PLoS ONE. 2016;11(10):e0164839 10.1371/journal.pone.0164839 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Vanhegan IS, Malik AK, Jayakumar P, Ul Islam S, Haddad FS. A financial analysis of revision hip arthroplasty: the economic burden in relation to the national tariff. J Bone Joint Surg Br. 2012;94(5):619–23. 10.1302/0301-620X.94B5.27073 [DOI] [PubMed] [Google Scholar]
  • 8.Wylde V, Blom AW. The failure of survivorship. J Bone Joint Surg Br. 2011;93(5):569–70. 10.1302/0301-620X.93B5.26687 [DOI] [PubMed] [Google Scholar]
  • 9.Porter M, Armstrong R, Howard P, Porteous M, Wilkinson JM. Orthopaedic registries—the UK view (National Joint Registry): impact on practice. EFORT Open Rev. 2019;4(6):377–90. 10.1302/2058-5241.4.180084 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.National Joint Registry for England, Wales, Northern Ireland and Isle of Man. 15th Annual report—Unit-level activity and outcomes. 2018.
  • 11.National Joint Registry for England, Wales, Northern Ireland and Isle of Man. 15th Annual report—Prostheses used in hip, knee, ankle, elbow and shoulder replacement procedures 2017. 2018.
  • 12.Chatfield C. Reducing variations in care. BMJ. 2017;359:j5574 10.1136/bmj.j5574 [DOI] [PubMed] [Google Scholar]
  • 13.Briggs T. A national review of adult elective orthopaedic services in England. Getting It Right First Time 2015. [cited 2020 Jan] Available from: http://gettingitrightfirsttime.co.uk/surgical-specialty/orthopaedic-surgery/. [Google Scholar]
  • 14.Kaplan EL, Meier P. Nonparametric Estimation from Incomplete Observations. J Am Stat Assoc. 1958;53(282):457–81. [Google Scholar]
  • 15.Royston P, Parmar MK. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med. 2002;21(15):2175–97. 10.1002/sim.1203 [DOI] [PubMed] [Google Scholar]
  • 16.Lambert P. Standardized survival functions blog. 2017 Aug 6 [cited 2020 July 7] Available from https://pclambert.net/software/stpm2_standsurv/standardized_survival/.
  • 17.Smith AJ, Dieppe P, Vernon K, Porter M, Blom AW. Failure rates of stemmed metal-on-metal hip replacements: analysis of data from the National Joint Registry of England and Wales. Lancet. 2012;379(9822):1199–204. 10.1016/S0140-6736(12)60353-5 [DOI] [PubMed] [Google Scholar]
  • 18.Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393 10.1136/bmj.b2393 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Gooberman-Hill R, Burston A, Clark E, Johnson E, Nolan S, Wells V, et al. Involving patients in research: considering good practice. Musculoskeletal Care. 2013;11(4):187–90. 10.1002/msc.1060 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Helen Howard

21 Jan 2020

Dear Dr Evans,

Thank you for submitting your manuscript entitled "What makes a Total Hip Replacement last longer, the implant or who puts it in?

Findings from the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man." for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by .

Login to Editorial Manager here: https://www.editorialmanager.com/pmedicine

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Helen Howard, for Clare Stone PhD

Acting Editor-in-Chief

PLOS Medicine

plosmedicine.org

Decision Letter 1

Emma Veitch

26 Apr 2020

Dear Dr. Evans,

Thank you very much for submitting your manuscript "What makes a Total Hip Replacement last longer, the implant or who puts it in? Findings from the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man." (PMEDICINE-D-20-00146R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by May 15 2020 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Emma Veitch, PhD

PLOS Medicine

On behalf of Clare Stone, PhD, Acting Chief Editor,

PLOS Medicine

plosmedicine.org

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Requests from the editors:

*We'd suggest using the general style for PLOS Medicine titles and therefore would simplify the second subpart of the title and just have "xxxyy: registry study" (rather than the current style which includes all the study settings).

*At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

*In the abstract, the findings presented currently don't present effect sizes and confidence intervals so it's hard for the reader to get a sense of what the main findings really show (for the crude and adjusted analyses), particularly as the p-value for the adjusted analysis is not stat sig by conventional cutoffs. If possible, the effect estimates and CI's from the main text should also be added here together with the p-values that are currently given.

*In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

*If possible, please change the in-text referencing style (which should be simple if referencing software was used) to giving the callouts in square brackets (eg, [1, 2] rather than superscript numbers). Many thanks

*Is it possible to clarify if the study had a prospective protocol or analysis plan? Please state this (either way) in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

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Comments from the reviewers:

Reviewer #1: Thanks for the opportunity to review your manuscript. My role is a statistical reviewer so I have concentrated on the data and analysis aspects of the manuscript, and the reporting of these. This manuscript aims to find whether individual or unit level factors lead to better outcomes in THR. I have provided overall comments, and then followed these with more specific queries. The page number refers to the page of the original word document not the compiled PDF.

The main aim of this study is to disentangle individual (i.e. patient characteristics and implant types) from unit level factors. The unit effect (exemplar vs. other) is estimated as a fixed effect, without accounting for the multi-level structure of the data. For this type of research question a common approach would be to estimate a multi-level model for the outcome (a shared frailty model in this case of time-to-event data) and use the random intercepts to describe the variation, and then add individual and unit level factors to estimate whether accounting for composition (patient characteristic at sites) explains differences in the outcome.

One of they key strengths of the manuscript is the large dataset from the joint replacement registry. My issues with the analysis come from the approach used to estimate unit vs individual effects and the missing data.

One of the fundamental parts of the study design is the selection of the RD&E unit as the reference unit for good survival outcomes for THRs. The last available report of the joint replacement registry highlighted several other units with better than expected outcomes for THR. It's not clear to me from this manuscript why just this individual unit was selected when there are apparently other high-performance units. Where these other units with good outcomes pooled with the rest of the units in the comparison of RD&E vs. other?

The two major problems I can see with this approach is that it doesn't account for clustering within units (so the SE will be incorrect) and that there is no way to asses the variation across all of the units and come down to comparison of one selected unit with the rest in the registry.

The reporting of missing data isn't clear - ~30% of patients have missing BMI and presumably aren't included in the main analysis, but from Figure 1 I don't understand if these patients are excluded in this pathway, or if the exclusions used to create the complete case dataset are made after this point. It would be helpful for each of the covariates used in the analysis if a table reporting how many patients had missing data for each of the variables by the strata used to presented Table 1.

A complete case analysis was used and justified as 'MI can introduce bias if not truly missing at random'. Unless done very poorly MAR approaches are also robust to when data is MCAR, and should be considered a more principled approach over complete-case analysis. The registry data does appear to have gaps in some of the variables that may make MI difficult to use (but given the description that exists it doesn't look impossible). I don't think this analysis necessarily needs to include MI, rather the limitations of a complete-case analysis should be adequately described in the discussion.

P4, L31. It's probably a result of the compiled PDF but Figure 1 looks like it has some image issues (it's a bit blurry).

P5, L1. Are there multiple patient records in this dataset (i.e. sequential unilateral HR)?

P5, L22. What proportion of censored outcomes were administrative (end of data availability) and how many were for death? Death is a competing risk for implant failure and it seems likely that the censoring from death and failure are not independent. Is the proportion of censoring from death small enough it is unlikely change implant failure hazard rates accounting for competing risks?

P7, L26. BMI is included into the standard categories, but in my experience with patient data from joint replacement registries the number of patients with BMI >35 is much higher than seen in the general population. Is s the case for THRs in the UK? Does including the extended categories (particularly a 'super-obese' >40 category) provide additional adjustment that gives different results?

Supplementary Fig1 + 2. For the Supplementary material, it would be helpful if there was an explanatory section for the graphical checks of non-proportionality and GOF - a brief explanation of what the diagnostic

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Reviewer #2: Thank you for the opportunity to review this work.

The work highlights the importance of informed and consistent surgeon choices rather than surgical skill in order to improve patient outcomes using NJR data. It demonstrates that the results achieved by a positive outlier centre are achievable by all surgeons rather than the elite few and uses robust data and methodology to do this.

The benefits of reducing variation and revision rates by adopting best practice are not just patient related but have far reaching cost implications on the health care system.

Using one centre that uses a single femoral stem and is a positive outlier in the NJR reduces bias and allows for this in-depth study, and is only achievable with analysis of registry data.

The use of flexible parametric survival analysis by the authors is commended, however, the majority of readers may not be familiar with this method. I would therefore suggest the authors expand on this method and why it is most suitable for this study. The use of sensitivity analysis and PPI also add to the validity if the findings.

In particular:

Methods:

Please expand on the suitability and use of FPSA.

Results:

In the adjusted analyses section, the restricted analysis is important as it is the construct that is the most important factor rather than the individual components and there are still acceptable numbers in this restricted analysis. Consider simplifying the sentence regarding the null hypothesis with regard to RMST as it is clunky and difficult to interpret, Please expand on the the difference in RMST between groups changing over time in Figure 4, as this may not be clear to some readers.

For the sensitivity analysis, the performance of the five most commonly implanted stems indicates that not all stems of the same mode of fixation are comparable. This is an important finding and may be lost in the supplementary material. Consider including in the main text or expanding on this finding.

Discussion:

The authors suggest that implant choice is responsible for the 'better than expected' results at the RD&E rather than surgeon skill. Does consistency also contribute to this and can this be measured? Are there any other centres that are as consistent in their implant choice (especially if it is a different implant) and are their results comparable? If surgeons consistently use the same implant, even if it is a poorer performing implant, do they have improved performance compared with less consistent surgeons?

In summary, this is an important study with a valuable message that has been examined using strong methodology with registry data. It identifies factors that are easily modifiable (i.e. implant choice) for a surgeon to improve implant survivorship and confirms the findings of the GIRFT initiative. It is concisely and clearly written, with appropriate methodology and conclusions, and provides novel information that is important within the discipline.

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Reviewer #3: The authors address an important issue within the arthroplasty surgery community, what is the main driver for outcome (defined as revision) the implant or the surgeons (and his team) who puts it in.

The aim was to investigate whether "better than expected" survival results of one type of implant within one clinic was due to the unit or the implant.

For analysis they used Flexible parametric survival analysis (FPSA). The latter has the advantage of taken time-depended covariates into account, but this analysis technique may also show effects which may not be present. The authors assessed FPSA models for goodness of fit against KM curves.

The NJR data were complete 95% primary and 90% revision, what were those data for the RD&E unit?

What was the minimum number of TH procedures each surgeon did at the RD&E unit. For that matter in the discussion section it could be discussed that all surgeons performed at least x number, which also excludes a surgical factor being a confounder in the outcome "revision".

Strengths Weakness section

Please explore more e.g. why FPSA seems to better compared to KM with proportional hazard models ; competing risk analysis. But FPSA may have some disadvantage as well, it may "dictate" the survival curve as such. The latter will give more insight to the clinical reader on advantages and disadvantages of these two widely sued techniques.

I would also recommend a statistician for review

Nevertheless, in conclusion I would congratulate the authors with their work

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Any attachments provided with reviews can be seen via the following link:

[LINK]

Decision Letter 2

Caitlin Moyer

2 Jul 2020

Dear Dr. Evans,

Thank you very much for re-submitting your manuscript "What makes a Total Hip Replacement last longer, the implant or who puts it in? A registry study" (PMEDICINE-D-20-00146R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and it was also seen again by one reviewer. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Jul 09 2020 11:59PM.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

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Requests from Editors:

1.Data availability statement: Please provide the complete access information (a web address or contact email address) for access to the study data.

2.Response to reviewer 3: Please do include the surgeon data description and mention in the discussion of how surgeon factors into your interpretation.

3.New reviewer 1 comments: Please do include a sentence in the manuscript regarding the issue of multiple implants from the same patient in the dataset being a rare risk, and please note in the Methods regarding the BMI categories that were used, as requested by the reviewer.

4.Title: Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon). Please also include the setting of the study in the title.

We suggest: “Factors associated with implant survival following total hip replacement surgery: A registry study of data from the National Joint Registry of England, Wales, Northern Ireland, and the Isle of Man” or similar.

5.Abstract: Background: Please revise the final sentence to “We aimed to investigate whether the statistically improved implant survival of THRs in a high performing unit is associated with the implants used or other factors at that unit, such as surgical skill.” to avoid implications of causality, which your study cannot address.

6.Abstract: Methods and Findings: Please revise the sentence “Either the selected “exemplar” unit or all others combined.” to clarify what this means.

7.Abstract: Methods and Findings: Please provide some information on the number of hospitals represented, and other summary demographic information to lend context here.

8.Abstract: Methods and Findings: For the second to last sentence, please revise to “A limitation is that this study is observational and conclusions regarding causality cannot be inferred.” or similar, to clarify.

9.Abstract: Conclusions: Please interpret the study based on the results presented in the abstract, emphasizing what is new without overstating your conclusions. For example, your conclusions speak to an association between performance and factors not mentioned in the Methods and Findings section of the abstract. Please revise the abstract so that the Conclusions are an interpretation of what you present in the Methods and Findings.

In addition to clarifying this, we suggest revising the first sentence to: “Our results suggest that the “better than expected” performance of an exemplar centre is associated with implant choice rather than process factors such as surgical skill or experience.” to avoid implying causality.

10. Author Summary: Why Was This Study Done? Please combine bullet points to reduce the total number; we suggest:

--In general, Total Hip Replacement (THR) is safe and effective at reducing pain and restoring

mobility to people with end-stage arthritis of the hip.

--In England and Wales in 2017 over 822 different types of hip replacement were used, and different brands of hip replacement have been shown to have varying survival rates at different follow-up timepoints. Reducing variation in outcomes following surgery is an important aim of the National Health Service (NHS) in England and Wales.

--A national database of all hip replacements in England and Wales (the National Joint Registry) has shown variation in survival rates between different hospitals, and a few hospitals are highlighted by the database for having better survival rates than the others.

11. Author Summary: What Did the Researchers Do and Find?: Please add one bullet point describing the study (objective and method).

12. Author Summary: What do these findings mean?: We suggest the following revision:

--These findings suggest that the better results seen in this one hospital are not associated with the skill of the surgeon, or the set-up of the hospital but are associated with the choice of hip replacement.

--Future studies are needed to determine if this is also the case across other brands of hip replacement, and to determine whether the choice of implant is similarly associated with implant survival across other specialties.

13. Methods: Under “Sensitivity Analyses”: Please define the exact ranges used and provide the ranges for SES deciles and BMI WHO categories: “Socioeconomic status was assessed using deciles of the Index of Multiple Deprivation (IMD) organised by Lower-Layer Super Output Areas (LSOA) and BMI was treated as a categorical variable using World Health Organisation categories.” Please do include the BMI table provided in the response to reviewer 1, at least as supporting information.

14. Methods: Please provide the name(s) of the institutional review board(s) that provided ethical approval, and please indicate whether participant consent was obtained and the manner of consent (written or oral).

15. Methods: Please provide information on the patient data included in the NJR, the centers represented (region, etc.) and how participant data were obtained (please explain what is presented in Figure 1).

16. Results. The first sentence mentions “exclusions” and there does not seem to be any text here or in the methods describing how or why data were excluded from analysis. Please supply information describing excluded data.

17. Results: Adjusted analyses: Please refer to specific supplementary figures and tables, rather than generally referring to “supplementary material”

18. Discussion: Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

19. Please incorporate the section “Strengths and weaknesses of the study in relation to other studies” (“We are unaware of any studies to date investigating the reasons why one unit achieves better THR survival than others.”) into the rest of the discussion, this does not need to be an independent section.

20. Discussion: Unanswered questions and future research: Please revise this sentence to “Although implant selection appears to be associated with improved survival in THR, it is yet to be seen whether the same phenomenon is true in other branches of medicine heavily reliant on implantable devices. Further research in other areas is warranted to investigate this effect.” to remove implying causality.

21. Discussion: Please expand on the discussion of unanswered questions and implications for clinical practice.

22. Conclusion: Please revise this sentence to: “In this study, we found evidence suggesting that implant selection is associated with the long-term survival of total hip replacements rather than factors specific to a high performing unit.” to avoid causal implications.

23. Please remove the sections: Contributions, Ethical Approval, Competing Interests, Transparency Statement, Role of the Funding Source, Disclaimer- and please make sure all of the information contained is submitted within the manuscript submission form.

24. References: References: Please place the in-text citation in brackets before the punctuation mark, like this [1].

Please use the "Vancouver" style for reference formatting, and see our website for other reference guidelines https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

For example, please check reference 5, and 16, and please make sure journal abbreviations are consistent with Vancouver style.

25. Figure 2: Please define, in the legend, that the shaded region represents the 95% CI for each line. Please consider using different colors for the different lines, as it is difficult to differentiate the shades of gray. Please spell out “operation” or use something descriptive such as “THR” procedure, in the X axis label. Please use evenly spaced markers for the years on the X axis, unless there is a reason not to do so. Please define the abbreviation for THR and KM in the legend.

26. Figure 3: Please define THR, ASA and RD&E in the legend. Please spell out “operation” or use something descriptive such as “THR” procedure, in the X axis label. Please use evenly spaced markers for the years on the X axis, unless there is a reason not to do so. In the legend, please explain all the panels (3a, 3b, etc).

27. Figure 4: Please define RMST in the legend. Please define the shaded region as the 95% CIs. Please explain between which groups the difference is illustrating.

28. Figures: Please provide descriptive legends for all figures (including those in Supporting Information files).

29. Table 1: Please define abbreviations for NHS, BMI, THR, ASA in the legend.

30. Supplementary material file: For all graphs please change “Years post op” to Years post operation or THR or similar. Please provide titles and legends for each individual table and figure in the Supporting Information (Figure 5, Table 1 and Table 2 have no legends), and make sure that all abbreviations used within figures and tables are spelled out in the legends.

31. Checklist: Please ensure that the study is reported according to the STROBE guideline, and include the completed STROBE checklist as Supporting Information. When completing the checklist, please use section and paragraph numbers, rather than page numbers.

Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

Comments from Reviewers:

Reviewer #1: Thanks for the revised manuscript and detailed responses to my original queries. Overall, I think this manuscript is looking good with just a few suggested minor changes

With the research question further clarified, I agree with the authors that a multi-level model approach is not required for this study, with the focus on the type of implant rather than surgical unit. Being able fit the more sophisticated flexible survivals models is distinct advantage here so I am satisfied with the approach used.

The information about THR and mortality was helpful thank you and being able to look at these references was reassuring. One of my original comments was about the possibility of multiple implants from the same patient in the dataset. If the risk is very rare then it is very unlikely to change the results by accounting for this, I would be happy if you could include a sentence explaining this with a reference.

The changes to the comments about missing data, and the clarifications about the exclusions around missing patients are now clear and I am happy with the changes. The additional information about BMI was useful for me but I agree that this doesn't necessarily need to be added to the manuscript if BMI was only used in one of the sensitivity analyses. A note in the methods that you have used these extended categories (relative the usual <25, 25-<30, 30+ which most people try to get away with) would be helpful.

The revised supplementary material is helpful and I think this should be a helpful for a general audience who want to understand the flexible survival analysis.

I agree with the comments you made in response to reviewer 3 - the FPS should allow the curve to better fit the data and even if the data showed PHs it would still give a valid result (and one similar to Cox in that situation).

The question you raise in the last sentence of the strengths and weaknesses section is a good one - next project? (no changes required, just a comment)

Any attachments provided with reviews can be seen via the following link:

[LINK]

Decision Letter 3

Caitlin Moyer

30 Jul 2020

Dear Mr Evans,

On behalf of my colleagues and the academic editor, Dr. Rob G.H.H. Nelissen, I am delighted to inform you that your manuscript entitled "Factors associated with implant survival following total hip replacement surgery: A registry study of data from the National Joint Registry of England, Wales, Northern Ireland, and the Isle of Man" (PMEDICINE-D-20-00146R3) has been accepted for publication in PLOS Medicine.

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 STROBE Checklist. Annotated STROBE checklist detailing how this study meets the criteria laid out in the STROBE statement.

    STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

    (DOC)

    S1 Fig. Demonstration of goodness of fit of flexible parametric survival analysis model for total hip replacements using the Exeter V40 femoral stem.

    Goodness of fit was assessed visually using the above figure as well as by assessment of log-likelihood of different models. A model with 5 knots was chosen as further knots provided more complexity with little improvement in log-likelihood.

    (TIF)

    S2 Fig. Demonstration of nonproportionality of hazard of revision.

    The above figure demonstrates that when using a flexible parametric survival analysis model that allows the hazard of failure to vary with time to compare TD and PH models. There is an apparent difference between the hazard of failure at the exemplar center and in all other units (the solid lines). The fact that these solid lines cross is highly indicative of the fact that the hazards are not proportional through the entire follow-up of the study. PH, proportional hazard; TD, time dependent.

    (TIF)

    S3 Fig. FPSA complete case analysis adjusted for age, sex, American Society of Anesthesiology score, and socioeconomic status.

    Results presented for a 68-year-old female patient with an American Society of Anesthesiology score of 2 and in the 10th decile of IMD organized by LSOA. FPSA, flexible parametric survival analysis; IMD, Index of Multiple Deprivation; LSOA, Lower Layer Super Output Area.

    (TIF)

    S4 Fig. FPSA complete case analysis adjusted for age, sex, American Society of Anesthesiology score, socioeconomic status, and body mass index.

    Results presented for a 68-year-old female patient with an American Society of Anesthesiology score of 2 and in the 10th decile of IMD organized by LSOA and body mass index in World Health Organization category 2. FPSA, flexible parametric survival analysis; IMD, Index of Multiple Deprivation; LSOA, Lower Layer Super Output Area.

    (TIF)

    S5 Fig. Comparison of the all-cause construct survival of the 5 most used cemented stems of all time in combination with any cup.

    A comparison of the probability of all-cause revision (1 –Kaplan–Meier) for all constructs using the 5 most frequently implanted cemented femoral stems, demonstrating the differences in revision estimates between these stems. This suggests that the results demonstrated in this study may be achievable with other femoral stems.

    (TIF)

    S1 Table. Distribution of BMI across categories.

    A comparison of the distribution of BMI between the 2 exposure categories (Royal Devon & Exeter hospital and all other hospitals combined). BMI, body mass index.

    (DOCX)

    S2 Table. Distribution of missing data.

    Table detailing the distribution of missing data between the exposure categories (Royal Devon & Exeter hospital and all other hospitals combined).

    (DOCX)

    S3 Table. At-risk table.

    Table demonstrating the number of total hip replacements at risk at each time point following operation. For use in the interpretation of previous survival graphs.

    (DOCX)

    Attachment

    Submitted filename: V1.2_PLOS_response_to_reviewers.docx

    Attachment

    Submitted filename: V1.0_third_revision_responses.docx

    Data Availability Statement

    Data cannot be shared publicly. Data are available from the NJR research subcommittee researchers who meet the criteria for access to confidential data. Access to the data used in this study can be requested via njrresearch@hqip.org.uk. Full details of how to request NJR data for research can be found at: http://www.njrcentre.org.uk/njrcentre/Research/Research-requests.


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