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Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

https://doi.org/10.1007/s00167-021-06692-8

1 3

KNEE

Hospital volume–outcome relationship intotal knee arthroplasty:

asystematic review anddose–response meta‑analysis

C.M.Kugler1 · K.Goossen1· T.Rombey1,2· K.K.DeSantis1,3· T.Mathes1· J.Breuing1· S.Hess1· R.Burchard4,5,6·

D.Pieper1

Received: 21 April 2021 / Accepted: 6 August 2021 / Published online: 8 September 2021

Abstract

Purpose This systematic review and dose–response meta-analysis aimed to investigate the relationship between hospital

volume and outcomes for total knee arthroplasty (TKA).

Methods MEDLINE, Embase, CENTRAL and CINAHL were searched up to February 2020 for randomised controlled trials

and cohort studies that reported TKA performed in hospitals with at least two different volumes and any associated patient-

relevant outcomes. The adjusted effect estimates (odds ratios, OR) were pooled using a random-effects, linear dose–response

meta-analysis. Heterogeneity was quantified using the I2-statistic. ROBINS-I and the GRADE approach were used to assess

the risk of bias and the confidence in the cumulative evidence, respectively.

Results A total of 68 cohort studies with data from 1985 to 2018 were included. The risk of bias for all outcomes ranged

from moderate to critical. Higher hospital volume may be associated with a lower rate of early revision ≤ 12months (narra-

tive synthesis of k = 7 studies, n = 301,378 patients) and is likely associated with lower mortality ≤ 3months (OR = 0.91 per

additional 50 TKAs/year, 95% confidence interval [0.87–0.95], k = 9, n = 2,638,996, I2 = 51%) and readmissions ≤ 3months

(OR = 0.98 [0.97–0.99], k = 3, n = 830,381, I2 = 44%). Hospital volume may not be associated with the rates of deep infec-

tions within 1–4years, late revision (1–10years) or adverse events ≤ 3months. The confidence in the cumulative evidence

was moderate for mortality and readmission rates; low for early revision rates; and very low for deep infection, late revision

and adverse event rates.

Conclusion An inverse volume–outcome relationship probably exists for some TKA outcomes, including mortality and

readmissions, and may exist for early revisions. Small reductions in unfavourable outcomes may be clinically relevant at the

population level, supporting centralisation of TKA to high-volume hospitals.

Level of evidence III.

Registration number The study was registered in the International Prospective Register of Systematic Reviews (PROSPERO

CRD42019131209 available at: https:// www. crd. york. ac. uk/ prosp ero/ displ ay_ record. php? Recor dID= 131209).

Keywords Total knee arthroplasty (TKA)· Knee osteoarthritis· Hospital volume· Hospital volume–outcome relationship·

Systematic review· Dose–response meta-analysis

* C. M. Kugler

char[email protected]

1 Institute forResearch inOperative Medicine (IFOM),

Witten/Herdecke University, Ostmerheimer Str. 200,

51109Cologne, Germany

2 Department ofHealth Care Management, Technische

Universität Berlin, Straße des 17. Juni 135, 10623Berlin,

Germany

3 Leibniz Institute forPrevention Research andEpidemiology

(BIPS), Achterstr. 30, 28359Bremen, Germany

4 Department ofOrthopaedics andTrauma Surgery,

Lahn-Dill-Kliniken, Rotebergstr. 2, 35683Dillenburg,

Germany

5 Department ofHealth, Witten/Herdecke University,

Alfred-Herrhausen-Straße 50, 58448Witten, Germany

6 Department ofOrthopaedics andTraumatology, University

ofGiessen andMarburg, Baldingerstraße, 35032Marburg,

Germany

2863Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

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Abbreviations

CI Confidence interval

GRADE Grading of recommendations, assessment,

development, and evaluation

k Number of studies

n Patients with event (outcome)

N Number of patients at risk

OR Odds ratio

PRESS Peer review of electronic search strategies

PRISMA Preferred reporting items for systematic

reviews and meta-analyses

ROBINS-I Risk of bias in non-randomised studies of

interventions

SWiM Synthesis Without Meta-analysis

TKA Total knee arthroplasty

Introduction

Total knee arthroplasty (TKA) can improve pain and func-

tion in patients with end-stage knee osteoarthritis [99] and

is increasingly performed worldwide [48, 87]. Unfavourable

outcomes of TKA include revision surgery, deep infection,

readmissions, and mortality, though rates of mortality are

low [12, 24, 87].

A hospital volume–outcome relationship exists for vari-

ous surgical procedures, meaning that higher hospital vol-

ume is associated with improved health outcomes [59, 84].

Some countries have therefore centralised selected surgi-

cal procedures to high-volume hospitals [70, 86]. A vol-

ume–outcome relationship may also exist for TKA [36, 84,

106]. Previous systematic reviews [26, 62, 107] are likely

out of date, and have methodical limitations. The only pub-

lished meta-analysis compared TKA outcomes only between

the highest and lowest hospital volume categories [107].

The aim of this systematic review was to quantify the

relationship between hospital volume and patient-rele-

vant outcomes of TKA including complications using a

dose–response meta-analysis. The hypothesis was that, as

with other surgical procedures, a higher hospital volume

would be associated with better patient-relevant outcomes

of TKA.

Methods

The reporting of this systematic review adheres to the Pre-

ferred Reporting Items for Systematic Reviews and Meta-

Analyses (PRISMA) 2020 Statement [80]. The protocol

was registered prospectively in the Prospective Register

of Systematic Reviews (PROSPERO registration number

CRD42019131209[89] and published upfront[90].

Systematic literature search

The search strategies were developed with the support of

an experienced librarian according to the Peer Review of

Electronic Search Strategies (PRESS) guideline [63]. The

electronic search was conducted without any limits in four

databases (MEDLINE, Embase, CENTRAL, CINAHL; Sup-

plementary Material 1) from inception to February 2020 and

in trial registers (ClinicalTrials.gov, German Clinical Study

Further sources of literature included conference proceed-

ings, reference lists of included studies, forward citation

searching (Web of Science) and contact with experts (Sup-

plementary Table1). No language restriction was applied.

Articles published in languages other than English, German,

or Italian were sent for professional translation.

Study selection

Studies with any design that (1) involved patients under-

going primary and/or revision TKA, (2) reported data for

at least two different hospital volumes, and (3) analysed

at least one patient-relevant outcome were included (see

Supplementary Table2 for a full list of eligibility criteria).

After the duplicates were removed, two reviewers inde-

pendently screened the titles and abstracts of all retrieved

sources in EndNote (Clarivate Analytics, version X9.1)

and assessed the full text of all potentially eligible articles.

Any discrepancies were resolved by consensus or, when

necessary, by consultation with a third reviewer.

Data extraction

Data were extracted independently by two reviewers

using standardised data extraction sheets. Any discrepan-

cies were resolved by consensus. The data items included

study, patient, hospital and surgeon characteristics; time

and country of data collection; data source; hospital vol-

ume definitions; TKA details; patient-relevant outcomes;

and statistical analysis details (effect size types, confidence

intervals, and confounding factors). The primary outcome

was the early revision rate ≤ 12months after TKA. The

secondary outcomes were any other patient-relevant out-

comes that were classified according to clinical experience

as ‘main outcomes’ [41] or ‘other outcomes’. All extracted

outcomes are summarised and defined in Supplementary

Table3. Study results (adjusted and/or unadjusted) were

extracted separately for each hospital volume category and

outcome. If data were missing or incompletely reported,

study authors were contacted via email[37].

2864 Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

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Risk ofbias andpublication bias

The risk of bias in the included studies was independently

assessed at the outcome level by two reviewers using the

Risk Of Bias In Non-randomised Studies of Interventions

(ROBINS-I) tool [108]. For any outcomes with at least

ten studies, assessment of publication bias was planned by

visual inspection of the funnel plots for asymmetry and by

applying Egger’s [31] and Begg’s tests [10].

Statistical analysis

Hospital volume was defined as the mean annual number

of patients undergoing TKA. Hospital volume categories

were standardised using their midpoints. For individual

study outcomes, odds ratios (ORs) with 95% confidence

intervals (95% CIs) were converted such that the lowest

volume category was the reference.

Individual study results were plotted to visually inspect

linearity (e.g. better outcomes with increasing volume)

for each outcome. A random-effects linear dose–response

meta-analysis according to Greenland and Longnecker

[38] was used to pool ORs for outcomes reported in at least

three studies with sufficient data (Supplementary Mate-

rial 2). For each outcome, measurements ≤ 3months after

TKA were aggregated in one analysis and those > 3months

in another. Revisions were aggregated in three analy-

ses: ≤ 12months, 1–5years, and 6–10years after TKA.

Wherever the overlap among two or more study samples

exceeded 20%, only one study was selected for meta-anal-

ysis based on data completeness, sample size, and the suit-

ability of the volume categories as criteria (Supplementary

Tables4, 5, 6). The main dose–response meta-analysis

was computed using the ‘best-adjusted’ effect estimates.

These were the ORs adjusted for at least one confounding

variable, including age, gender, and comorbidities, but not

for post- or within-intervention variables such as surgeon

volume. Heterogeneity between studies was assessed using

the Q test and I2-statistic [46]. Four sensitivity analyses

(Supplementary Material 3) were conducted; the first

analysis compared extreme volume categories (highest vs.

lowest), and the second, third and fourth analyses (post

hoc) studied the influence of confounding variables. An

additional post hoc dose–response meta-analysis was con-

ducted using ‘best available’ (adjusted and unadjusted)

effect estimates. All meta-analyses were performed with

R 3.6.3 (R Foundation for Statistical Computing, Vienna,

Austria) using the metafor and dosresmeta packages [25,

116]. Outcomes that were not suitable for meta-analysis

(Supplementary Material 2) were synthesised narratively

using the Synthesis Without Meta-analysis (SWiM) guide-

line (Supplementary Material 4) [20].

Grading theevidence

Confidence in the cumulative evidence was evaluated

using the Grading of Recommendations, Assessment,

Development, and Evaluation (GRADE) approach [19,

41, 91, 95, 113] and applying Murad’s approach [72] for

SWiM outcomes. Two reviewers independently graded

outcomes using GRADEpro GDT software [64] and

reached consensus during discussion.

Patient involvement

Potential TKA patients were asked for their opinions on

the hospital volume–outcome relationship for TKA and

their hospital preferences using qualitative methodology

(focus groups and interviews). The methods and results are

reported elsewhere [55].

Results

Study identification andselection

A total of 13,048 records were identified from electronic

databases and trial registers, and 2266 were identified from

reference lists of included articles, forward citation search,

websites, and author contact. Of 347 full-text reports,

269 were excluded (Supplementary Table7). This review

included 68 cohort studies reported in 78 articles [1–9, 13,

16–18, 21–24, 27–30, 32–35, 39, 40, 42–45, 47, 49–54,

57, 58, 60, 61, 65, 67, 68, 71, 73–79, 81–83, 85, 88, 92–94,

97, 98, 100–104, 110–112, 114, 115, 118–122] with data

representing the years from 1985 to 2018 (Fig.1).

Study andpatient characteristics

The majority of studies used data from North America,

while 22 used data from Europe, 9 from Asia and 1 from

Australia. The data were obtained from administrative

databases in 47 studies, clinical registries in 18 studies,

and questionnaires in three studies. The average number

of patients across all studies was 222,038 (data from 65

studies), with a median of 65% females (IQR 62–69%, data

from 56 studies). The patients had a weighted mean age

of 71years (data from 40 studies). Each study included a

median of 486 hospitals (IQR: 43–569, data from 51 stud-

ies). In 55 studies, the population was limited to primary

TKA patients, 12 included primary and revision TKA

patients, and one study did not specify the type of TKA.

The study and patient characteristics of studies report-

ing primary and main secondary outcomes are shown in

2865Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

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Table1, and the characteristics of all 68 included studies

are shown in Supplementary Table8.

Study results

Individual study results are reported for all adjusted or unad-

justed outcomes by hospital volume category in Supplemen-

tary Tables4 and 9, respectively, and are summarised for the

primary outcome (early revision rates) in Table2.

Risk ofbias

The risk of bias was moderate for 30 study outcomes, serious

for 168, and critical for 3 (Supplementary Table10). Bias

was suspected mostly due to potential confounding, since

most effect estimates were not appropriately adjusted for

age, gender, and comorbidity.

Primary outcome: early revision rate

A higher hospital volume may be associated with a lower

early revision rate (7 studies [5, 50, 54, 61, 65, 82, 83],

narrative synthesis Table 2, low certainty evidence).

Five studies with a high risk of bias, which accounted for

261,243of301,378 (87%) patients in total for this outcome

[50, 54, 61, 65, 83], reported lower revision rates for higher

volumes. In contrast, the only study with a moderate risk of

bias [5] found that a higher hospital volume (> 125 TKAs/

year) was associated with a higher early revision rate.

Main secondary outcomes

The results of the linear dose–response meta-analysis of

best-adjusted effect estimates are presented in Table3 (main

secondary outcomes), Supplementary Table11 (other sec-

ondary outcomes) and Supplementary Table12 (post hoc

linear dose–response meta-analysis using ‘best available’

effect estimates).

Revision

There was no evidence for a linear dose–response rela-

tionship between hospital volume and revision rate within

1–5years (OR = 0.96 per 50 TKAs/year increase, 95% CI

[0.86–1.07]; 5 studies [5, 50, 51, 54, 73], I2 = 98%, very low

certainty, Table3). This finding was robust to sensitivity

analyses (Supplementary Tables13, 14, 16).

Fig. 1 PRISMA flow diagram showing selection of articles for review

2866 Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

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Table 1 Study characteristics with primary and main secondary outcomes

Study (refer-

ences)

Study characteristics Patients’ characteristics Volume categories (per year) Results

Type of

funding

Country

(region)

Primary data

source

Data coll.

(years)

No. of hos-

pitals

No. of

patients

% Female Age (years) Type Upper limits;

lower limit

of highest

category

Patient-rel-

evant study

outcomes

Authors’

conclusions

favour

Singh 2011

[98]

Non-profit USA (PA) Admin 2001–2002 169 19,418 65% Mean (IQR):

69 (60–75)

Thresholds 25; 100;

200; ≥ 201

Mortality,

infection,

Higher vol-

ume

Soohoo 2006

[102]

None USA (CA) Admin 1991–2001 413 222,684 62% Mean ± SD:

69 ± 10

Hospital

quintiles*

Means: 13;

50; 145

Mortality,

readmis-

sion, infec-

tion, AE

Higher vol-

ume

Wei 2010

[118]

None Taiwan Admin 2000–2003 295 31,618 74% Mean: 74 Hospital

quartiles*

6; 23; ≥ 24 Infection,

AE, LOS

n.r.

Yu 2019

[122]

Non-profit Taiwan Admin 2007–2008 437 30,828 75% Mean ± SD:

70 ± 8

Thresholds 74; ≥ 75 Readmission No evidence

for a differ-

ence

All studies were cohort studies. Unpublished data provided by study authors in italic

admin. administrative, AE postoperative adverse events, CA California, CO Colorado, coll. collection, compl. complications, FL Florida, GA Georgia, HCUP Health Care Utilization Project,

HI Hawaii, IL Illinois, IN Indiana, KY Kentucky, LOS length of stay, MAR Mid-Atlantic region, MD Maryland, MI Michigan, n.r. not reported, NC North Carolina, NRW North-Rhine Westphalia,

NWR North-West region, NY New York State, OH Ohio, ON Ontario, PA Pennsylvania, QoL quality of life, SN Saxony, TN Tennessee, UK United Kingdom, USA United States of America,

WA Western Australia

§ Includes funding by Zimmer, Smith & Nephew (medical devices co.)

† Includes funding by Bristol–Meyers Squibb (pharmaceutical co.)

$ Stryker Orthopaedics, Inc. (medical devices co.)

‡ Number of TKAs (number of patients not reported)

*Some quantiles were combined

2869Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

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Table 2 Study results and risk of bias for early revision

CI confidence interval, OR odds ratio, ROBINS-I risk of bias in non-randomised studies of interventions tool, TKA total knee arthroplasty

Study (references) Study characteristics Results Risk of bias

(ROBINS-I)

Country Time period (years) No. of patients Volume categories (TKA/year) Effect measure

Meehan 2014 [65]USA 2005–2009 120,538 1–49

50–100

101–200

> 200

Crude rate

2.52%

2.32%

1.96%

1.78%

Serious

Pamilo 2018 [82] Finland 1998–2010 59,696 No differences in revision rates between hospital

volume with data from only four hospitals with

similar TKA volumes

Serious

Manley 2009 [61]USA 1997–2004 53,971 1–25

26–100

101–200

> 200

Adjusted OR [CI]

1.91 [0.76–4.83]

1.38 [0.84–2.26]

1.17 [0.74–1.87]

1.00

Serious

Jeschke 2017 [50] Germany 2012 45,165 10–56

57–93

94–144

145–251

252–1648

Crude rate

5.19%

4.26%

3.81%

3.49%

3.34%

Serious

Arias-de la Torre 2019 [5] Spain 2005–2016 36,316 < 125

≥ 125

Crude rate;

Kaplan–Meier

rate [CI]

0.67%; 0.64%

[0.53–0.77%]

1.24%; 1.15%

[1.00–1.32%]

Moderate

Paterson 2010 [83] Canada 2000–2004 27,217 10–130

131–180

181–270

> 270

Adjusted OR [CI]

1.00

0.64 [0.39–1.04]

0.62 [0.42–0.91]

0.50 [0.34–0.72]

Serious

Kreder 2003 [54] Canada 1992–1996 14,352 < 48

48–113

> 113

Adjusted OR [CI]

2.23 [1.10–4.50]

1.57 [0.90–2.90]

1.00

Serious

Table 3 Results of linear dose–response meta-analysis of best-adjusted effect estimates (main secondary outcomes)

Statistically significant results in bold

CI confidence interval, I2 index for residual heterogeneity, k number of studies, n patients with event, N number of patients at risk, OR odds ratio,

ROBINS-I risk of bias in non-randomised studies of interventions tool, TKA total knee arthroplasty

a Overall risk of bias was serious in five studies and moderate in four studies. Since studies with moderate risk of bias dominated the results

(accounted for more than 80% of patients and events), we assume that the overall result is not seriously biased

b Overall risk of bias was serious in all studies

c Overall risk of bias was serious in all but one study, and moderate in one study

Outcome k(n/N) [%] I2Pooled OR [95% CI] for 50

TKA/year increase

Risk of bias

(ROBINS-I)

References

Mortality (≤ 3months) 9 4769/2,638,996 (0.2%) 51% 0.91 [0.87–0.95] Moderatea[45, 51, 52, 54,

60, 76, 83, 98,

104]

Infection (deep) (1–4years) 3 797/97,019 (0.8%) 0% 1.03 [0.97–1.09] Seriousb[4, 8, 74]

Revision (1–5years) 5 5498/163,520 (3.4%) 98% 0.96 [0.86–1.07] Seriousc[5, 50, 51, 54, 73]

Readmission (≤ 3months) 3 78,895/830,381 (9.5%) 44% 0.98 [0.97–0.99] Seriousc[7, 81, 122]

2870 Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

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The relationship between hospital volume and revision

rate within 6–10years was inconsistent (narrative synthesis,

5 studies [5, 9, 30, 81, 97], very low certainty).

Mortality

A higher hospital volume is likely associated with a lower

mortality rate ≤ 3months (OR = 0.91 per additional 50

TKAs/year, 95% CI [0.87–0.95]; 9 studies [45, 51, 52, 54,

60, 76, 83, 98, 104], I2 = 51%, moderate certainty, Table3,

Fig.2a). The direction of this relationship was robust to sen-

sitivity analyses (Supplementary Tables13–16), although

the pooled OR was no longer significant when the analysis

included only data that were also adjusted for surgeon vol-

ume (Supplementary Table15).

Deep infection

There was no evidence for a linear dose–response associa-

tion between hospital volume and the rate of deep infec-

tion within 1–4years (OR = 1.03 per 50 additional TKAs/

year, 95% CI [0.97–1.09], 3 studies [4, 8, 74], I2 = 0%, very

low certainty, Table3). However, the sensitivity analysis

comparing highest vs. lowest volume categories showed that

higher hospital volume may be associated with a higher rate

of deep infection (OR = 1.60; 95% CI [0.91–2.82], I2 = 54%,

Supplementary Table13).

Adverse events

Due to the heterogeneous clinical definitions of adverse

events in the primary studies (Supplementary Table3), this

outcome was not pooled. The relationship between hospital

volume and adverse event rates ≤ 3months was inconsist-

ent across studies in a narrative synthesis (Supplementary

Tables4, 9), and the certainty was very low based on 7 stud-

ies [52, 54, 60, 77, 94, 98, 118].

Readmission

A higher hospital volume was likely associated with a

slightly lower readmission rate ≤ 3months (OR = 0.98; 95%

CI [0.97–0.99], 3 studies [7, 81, 122], I2 = 44%, moderate

certainty, Table3, Fig.2b). The direction of this relationship

was robust to sensitivity analyses (Supplementary Tables13,

14), although the relationship was no longer statistically sig-

nificant when only unadjusted effect estimates were included

(Supplementary Table16).

Other secondary outcomes

Limited evidence (Supplementary Table6) showed that

higher hospital volume may be associated with lower rates

of the following outcomes:

1. Composite adverse events including mortal-

ity ≤ 3months [22, 40, 57, 98, 104],

2. Any infection ≤ 3 months [45, 98, 104, 118]

and > 3months [22, 54, 104]

3. Length of hospital stay [1, 32, 33, 45, 47, 51, 54, 60, 68,

76, 81, 83, 85, 110, 111, 118, 121],

4. Pneumonia ≤ 3months [52],

5. Superficial infection ≤ 3 months [7, 49, 78]

and > 3months [3, 71, 101],

6. ‘Surgical complications’ as a composite out-

come ≤ 3months [18, 40, 47, 83, 94],

7. Thromboembolic events ≤ 3months [45, 52, 98, 104]

and > 3months [104] and,

Fig. 2 Linear dose–response

meta-analysis for mortality (a)

and readmission (b)

2871Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

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8. Thrombophlebitis ≤ 3months [104] and > 3months

[104].

Hospital volume may be associated with func-

tion ≤ 3months in a U-shaped relationship [42, 49]. Spe-

cifically, postoperative mobility at discharge appeared to

be highest at hospital volumes of approximately 300–400

TKAs/year, and hospitals with lower or higher TKA vol-

umes had worse outcomes [49].

There was no evidence for a relationship between hospital

volume and the rates of the following outcomes:

1. Deep infection ≤ 3months [52, 58],

2. Mortality > 3months [22, 40, 57, 98, 104],

3. Myocardial infarction ≤ 3months [17, 52, 98],

Table 4 Summary of findings and certainty of evidence (GRADE)

CI confidence interval, I2 index for residual heterogeneity, k number of studies, n patients with event, N number of patients at risk, OR odds ratio,

ROBINS-I risk of bias in non-randomised studies of interventions tool, SWiM synthesis without meta-analysis, TKA total knee arthroplasty

Number of studies Study event rates Effect Certainty Importance

(n/N) [%] Extreme comparison

Relative [95% CI]

Absolute [95% CI]

Alternatively: SWiM

Dose–response OR per

50 TKAs/year increase

[95% CI]

Certainty rating

Reason for rating

Primary outcome: early revision (≤ 12months)

7 studies in SWiM [5,

50, 54, 61, 65, 82, 83]

N = 301,378 In 5 studies accounting for 87% of patients, higher

hospital volume was associated with lower rates of

early revision

⊕⊕⚪

Low

–2 for risk of bias

Critical

Main secondary outcomes

Mortality (all cause, ≤ 3months)

9 studies in meta-

analysis [45, 51, 52,

54, 60, 76, 83, 98,

104]

4769/2,638,996 (0.2%) OR 0.62

[0.48–0.79]

1 fewer per 1000

(from 1 to 0 fewer)

Linear dose–response

gradient

OR 0.91 [0.87–0.95]

⊕⊕⊕⚪

Moderate

–1 for risk of bias

–1 for inconsistency

+1 for dose–response

gradient

Critical

Infection (deep) (1–4years)

3 studies in meta-

analysis [4, 8, 74]

797/97,019 (0.8%) OR 1.60

[0.91–2.82]

5 more per 1000

[from 1 fewer to 15

more]

No evidence for a dose–

response association

⊕⚪⚪⚪

Very low

–2 for risk of bias,

–1 for imprecision

Critical

Revision (1–5years)

5 studies in meta-

analysis [5, 50, 51,

54, 73]

5,498/163,520 (3.4%) OR 0.99

[0.65–1.50]

0 fewer per 1 000

[from 12 fewer to 16

more]

No evidence for a dose–

response association

⊕⚪⚪⚪

Very low

–2 for risk of bias,

–1 for inconsistency,

–1 for imprecision

Important

Adverse events (≤ 3months)

7 studies in SWiM

[52, 54, 60, 77, 94,

98, 118]

N = 1,396,241 The effect of hospital volume on this composite

outcome was inconsistent across studies

⊕⚪⚪⚪

Very low

–2 for risk of bias,

–1 for inconsistency

Important

Revision (6–10years)

5 studies in SWiM [5,

9, 30, 81, 97]

N = 684,733 Results were inconsistent across studies ⊕⚪⚪⚪

Very low

–2 for risk of bias,

–1 for inconsistency

Important

Readmission (≤ 3months)

3 studies in meta-

analysis [7, 81, 122]

78,895/830,381 (9.5%) OR 0.85

[0.74–0.98]

13 fewer per 1000

[from 23 to 2 fewer]

Linear dose–response

gradient,

OR 0.98 [0.97–0.99]

⊕⊕⊕⚪

Moderate

–2 for risk of bias

+1 for dose–response

gradient

Important

2872 Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

1 3

4. Quality of life > 3months [115],

5. Readmission > 3months [51] and

6. Wound haematoma or secondary haemor-

rhage ≤ 3months [78].

Although patient satisfaction was reported in two stud-

ies [32, 92], we did not synthesise the results due to critical

risk of bias.

Certainty ofevidence

Table4 shows the GRADE assessment and summary of

findings for the primary and main secondary outcomes. The

individual GRADE domains and the certainty of evidence

for the other secondary outcomes are shown in Supplemen-

tary Tables5 and 6, respectively. The certainty of evidence

was moderate for 4 outcomes, low for 7 outcomes, very low

for 15 outcomes and not assessed for 1 outcome.

Discussion

The current systematic review reports the results of a

dose–response meta-analysis of 68 cohort studies that

assessed the relationship between hospital TKA volume and

patient-relevant outcomes. As hypothesised, higher hospital

TKA volume may be associated with a lower rate of early

revisions and is likely associated with small reductions in

mortality and readmission ≤ 3months after TKA. Earlier

systematic reviews by Critchley [26] and Stengel [107] also

found small reductions in mortality with increased hospital

TKA volume, whereas Marlow [62] found no evidence for

this association.

The certainty of evidence of the synthesised results was

reduced by the relatively high risk of bias resulting from the

observational design of the primary studies, which lies in the

nature of the topic. Furthermore, the selection of endpoints

for this systematic review was limited to morbidity and mor-

tality, which are more widely recorded than outcomes related

to function and quality of life. As a result, the association of

hospital volume with improvements in function, quality of

life, and pain reduction (the primary goals of TKA) could

not be assessed. Mortality may not be the most relevant end-

point to study from a patient perspective, and overall event

rates are very low. Nevertheless, the results may be may be

clinically relevant at the population level.

Higher hospital volume does not directly result in

improved patient outcomes but, rather, acts as a proxy

measure for quality [66, 70]. Three general explanatory

factors for the hospital volume–outcome relationship have

been identified for various medical procedures: level of

specialisation, hospital-level factors including nursing

staff and facilities, and compliance with evidence-based

processes [66]. In addition, there is a tendency for a sur-

geon volume–outcome relationship in TKA surgery [69].

Based on the results of this systematic review, surgeon

volume could constitute one aspect of the hospital vol-

ume–outcome relationship, since the meta-analysis no

longer showed a significant association with mortality

when only data adjusted for surgeon volume were included

(Supplementary Table15). In several types of cancer sur-

geries and cardiovascular procedures, surgeon volume

accounts for a large proportion of the effect of hospital

volume [15]. Therefore, the authors interpret hospital

volume as a proxy for quality, of which surgeon volume

is one element. Additional confounders exist, e.g. patient

characteristics [26] and changing suppliers of implant sys-

tems [105].

Understanding the volume–outcome relationship is

important in light of discussions regarding the centralisa-

tion of surgical procedures to specialised hospitals [14, 62].

These results suggest that centralising TKA surgery may

improve patient outcomes. A drawback of centralisation is

that it may increase patients’ travel burden and reduce access

for disadvantaged patients [14, 56, 66, 96].

Future studies should adhere to reporting guidelines

[11, 117] so that their data can be used more effectively

for further research. To evaluate whether the volume–out-

come relationship for TKA is non-linear, a future primary

study could use multinational registry data. Measurement

of patient-reported outcomes in the context of the hospital

volume–outcome relationship is desirable.

This systematic review has several limitations. First, the

results are based on a relatively small number of studies for

most outcomes, although a large number of studies were

included in this systematic review. This was because primary

studies did not report the same outcomes, and time points

or data required for the dose–response meta-analysis were

missing. Second, the small number of volume categories

in the primary studies may have hidden non-linear rela-

tionships, which could therefore have gone undetected by

a dose–response meta-analysis. Third, the applicability of

the results to other healthcare systems is limited because a

large proportion of data were collected in North America.

Fourth, there was considerable between-study heterogeneity

for most outcomes, probably due to inconsistent methodol-

ogy in primary studies, variation among healthcare systems

and regulatory approaches, and different periods of data

collection. Sources of heterogeneity could not be explored

by subgroup analysis because there were fewer than three

studies per subgroup for each outcome. However, when the

highest and lowest volume categories were compared, het-

erogeneity decreased, and pooled effect estimates showed

strengthened associations between hospital volume and out-

comes. Fifth, it was not possible to assess publication bias

because fewer than ten studies per outcome were included

2873Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

1 3

in the dose–response meta-analyses [109]. Because of these

limitations, conclusions should be drawn from the direction

and dimensions of the hospital volume–outcome associa-

tions rather than the exact numerical values of the pooled

effect sizes.

Conclusion

Policy makers need solid evidence when regulating surgical

procedures. The results for TKA show that there is moderate

to low certainty evidence for an inverse hospital volume–out-

come relationship for the outcomes of mortality, readmissions

and early revisions. These small reductions in unfavourable

outcomes may be clinically relevant at the population level.

This finding supports the centralisation of TKA surgery to

high-volume hospitals.

Supplementary Information The online version contains supplemen-

tary material available at https:// doi. org/ 10. 1007/ s00167- 021- 06692-8.

Acknowledgements The authors would like to thank Stefanie Bühn for

her help in searching study registries.

Author contributions CMK: data curation, formal analysis, inves-

tigation, methodology, validation, visualisation, writing—original

draft, and writing—review and editing. KG: conceptualisation, data

curation, formal analysis, investigation, methodology, project admin-

istration, validation, visualisation, writing—original draft, and writ-

ing—review and editing. TR: conceptualisation, data curation, formal

analysis, investigation, methodology, project administration, validation,

writing—original draft, and writing—review and editing. KKDS: data

curation, formal analysis, investigation, methodology, writing—origi-

nal draft, and writing—review and editing. TM: conceptualisation,

formal analysis, investigation, methodology, software, and writing—

review and editing. JB: formal analysis, investigation, and writing—

review and editing. SH: data curation, investigation, writing—original

draft, and writing—review and editing. RB: formal analysis, investiga-

tion, and writing—review and editing. DP: conceptualisation, funding

acquisition, supervision, and writing—review and editing.

Funding Open Access funding enabled and organized by Projekt

DEAL. This project was funded by the German Federal Ministry of

Education and Research (BMBF), Grant No. 01KG1805. The funder

played no role in the design, conduct, interpretation or dissemination

of the study.

Availability of data and material Additional details regarding meth-

odology and data are available upon reasonable request from the cor-

responding author.

Declarations

Conflict of interest The authors declare no conflict of interest. No ben-

efits in any form have been received or will be received from a com-

mercial party related directly or indirectly to the subject of this article.

Ethical approval We obtained ethics approval from the ethics commit-

tee of Witten/Herdecke University (Reference No. 54/2019) to involve

consumers (potentials TKA patients).

Informed consent Informed consents were obtained from all the par-

ticipants of the qualitative study.

Open Access This article is licensed under a Creative Commons Attri-

bution 4.0 International License, which permits use, sharing, adapta-

tion, distribution and reproduction in any medium or format, as long

as you give appropriate credit to the original author(s) and the source,

provide a link to the Creative Commons licence, and indicate if changes

were made. The images or other third party material in this article are

included in the article's Creative Commons licence, unless indicated

otherwise in a credit line to the material. If material is not included in

the article's Creative Commons licence and your intended use is not

permitted by statutory regulation or exceeds the permitted use, you will

need to obtain permission directly from the copyright holder. To view a

copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.

References

1. Adhia AH, Feinglass JM, Suleiman LI (2019) What are the risk

factors for 48 or more-hour stay and nonhome discharge after

total knee arthroplasty? Results from 151 Illinois hospitals,

2016–2018. J Arthroplasty 35(6):1466–1473

2. Amato L, Fusco D, Acampora A, Bontempi K, Rosa AC, Colais

P etal (2017) Volume and health outcomes: evidence from

systematic reviews and from evaluation of Italian hospital data.

Epidemiol Prev 41(5–6 Suppl 2):1–128

3. Anis HK, Mahmood BM, Klika AK, Mont MA, Barsoum

WK, Molloy RM etal (2020) Hospital volume and postop-

erative infections in total knee arthroplasty. J Arthroplast

35(4):1079–1083

4. Anis HK, Sodhi N, Klika AK, Mont MA, Barsoum WK,

Higuera CA etal (2019) Is operative time a predictor for

post-operative infection in primary total knee arthroplasty? J

Arthroplast 34(7):S331–S336

5. Arias-de la Torre J, Pons-Cabrafiga M, Valderas JM, Evans JP,

Martin V, Molina AJ etal (2019) Influence of hospital volume

of procedures by year on the risk of revision of total hip and

knee arthroplasties: a propensity score-matched cohort study.

J Clin Med 8(5):670

6. Arias-de la Torre J, Valderas JM, Evans JP, Martin V, Molina

AJ, Munoz L etal (2019) Differences in risk of revision and

mortality between total and unicompartmental knee arthro-

plasty. The influence of hospital volume. J Arthroplast

34(5):865–871

7. Arroyo NS, White RS, Gaber-Baylis LK, La M, Fisher AD,

Samaru M (2018) Racial/ethnic and socioeconomic dispari-

ties in total knee arthroplasty 30- and 90-day readmissions: a

multi-payer and multistate analysis, 2007–2014. Popul Health

Manag 22(2):175–185

8. Badawy M, Espehaug B, Fenstad AM, Indrekvam K, Dale H,

Havelin LI etal (2017) Patient and surgical factors affecting pro-

cedure duration and revision risk due to deep infection in primary

total knee arthroplasty. BMC Musculoskelet Disord 18(1):1–9

9. Badawy M, Espehaug B, Indrekvam K, Engesaeter LB, Havelin

LI, Furnes O (2013) Influence of hospital volume on revision

rate after total knee arthroplasty with cement. J Bone Jt Surg

Am 95(18):e131

10. Begg CB, Mazumdar M (1994) Operating characteristics

of a rank correlation test for publication bias. Biometrics

50(4):1088–1101

11. Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D,

Petersen I etal (2015) The reporting of studies conducted using

2874 Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

1 3

observational routinely-collected health data (record) state-

ment. PLoS Med 12(10):e1001885

12. Berstock JR, Beswick AD, López-López JA, Whitehouse MR,

Blom AW (2018) Mortality after total knee arthroplasty: a sys-

tematic review of incidence, temporal trends, and risk factors.

J Bone Jt Surg Am 100(12):1064–1070

13. Bini SA, Inacio MCS, Cafri G (2015) Two-day length of stay

is not inferior to 3 days in total knee arthroplasty with regards

to 30-day readmissions. J Arthroplast 30(5):733–738

14. Birkmeyer JD (2000) Should we regionalize major surgery?

Potential benefits and policy considerations. J Am Coll Surg

190(3):341–349

15. Birkmeyer JD, Stukel TA, Siewers AE, Goodney PP, Wennberg

DE, Lucas FL (2003) Surgeon volume and operative mortality

in the united states. N Engl J Med 349(22):2117–2127

16. Blum MA, Singh JA, Lee GC, Richardson D, Chen W, Ibrahim

SA (2013) Patient race and surgical outcomes after total knee

arthroplasty: an analysis of a large regional database. Arthritis

Care Res (Hoboken) 65(3):414–420

17. Bohm ER, Molodianovitsh K, Dragan A, Zhu N, Webster G,

Masri B etal (2016) Outcomes of unilateral and bilateral total

knee arthroplasty in 238,373 patients. Acta Orthop 87:24–30

18. Bottle A, Loeffler MD, Aylin P, Ali AM (2018) Comparison

of 3 types of readmission rates for measuring hospital and sur-

geon performance after primary total hip and knee arthroplasty.

J Arthroplast 33(7):2014-2019.e2012

19. Brozek JL, Akl EA, Compalati E, Kreis J, Terracciano L, Fioc-

chi A etal (2011) Grading quality of evidence and strength of

recommendations in clinical practice guidelines part 3 of 3.

The grade approach to developing recommendations. Allergy

66(5):588–595

20. Campbell M, McKenzie JE, Sowden A, Katikireddi SV, Brennan

SE, Ellis S etal (2020) Synthesis without meta-analysis (swim)

in systematic reviews: reporting guideline. BMJ 368:l6890

21. Charpentier PM, Srivastava AK, Zheng H, Ostrander JD, Hughes

RE (2018) Readmission rates for one versus two-midnight

length of stay for primary total knee arthroplasty analysis of the

Michigan Arthroplasty Registry collaborative quality initiative

(Marcqi) database. J Bone Jt Surg Am 100(20):1757–1764

22. Cheng CH, Cheng YT, Chen JS (2011) A learning curve of total

knee arthroplasty (tka) based on surgical volume analysis. Arch

Gerontol Geriatr 53(1):e5-9

23. Cram P, Lu X, Kates SL, Li Y, Miller BJ (2011) Outliers: hos-

pitals with consistently lower and higher than predicted joint

arthroplasty readmission rates. Geriatr Orthop Surg Rehabil

2(4):135–147

24. Cram P, Lu X, Kates SL, Singh JA, Li Y, Wolf BR (2012) Total

knee arthroplasty volume, utilization, and outcomes among

Medicare beneficiaries, 1991–2010. JAMA 308(12):1227–1236

25. Crippa A, Orsini N (2016) Multivariate dose-response meta-

analysis: the dosresmeta r package. J Stat Softw 72(1):1–15

26. Critchley RJ, Baker PN, Deehan DJ (2012) Does surgical volume

affect outcome after primary and revision knee arthroplasty? A

systematic review of the literature. Knee 19(5):513–518

27. D’Apuzzo M, Westrich G, Hidaka C, Jung Pan T, Lyman S (2017)

All-cause versus complication-specific readmission following

total knee arthroplasty. J Bone Jt Surg Am 99(13):1093–1103

28. Dailey L, Van Gessel H, Peterson A (2009) Two years of surgical

site infection surveillance in Western Australia: analysing vari-

ation between hospitals. Healthc Infect 14(2):51–60

29. Day MS, Karia R, Hutzler L, Bosco JA (2019) Higher hospital

costs do not result in lower readmission rates following total joint

arthroplasty. Bull Hosp Jt Dis 77(2):136–139

30. Dy CJ, Marx RG, Bozic KJ, Pan TJ, Padgett DE, Lyman S (2014)

Risk factors for revision within 10 years of total knee arthro-

plasty. Clin Orthop Relat Res 472(4):1198–1207

31. Egger M, Davey Smith G, Schneider M, Minder C (1997) Bias

in meta-analysis detected by a simple, graphical test. BMJ

315(7109):629–634

32. Featherall J, Brigati DP, Arney AN, Faour M, Bokar DV, Murray

TG etal (2019) Effects of a total knee arthroplasty care pathway

on cost, quality, and patient experience: toward measuring the

triple aim. J Arthroplast 34(11):2561–2568

33. Feinglass J, Amir H, Taylor P, Lurie I, Manheim LM, Chang

RW (2004) How safe is primary knee replacement surgery?

Perioperative complication rates in northern Illinois, 1993–1999.

Arthritis Rheum 51(1):110–116

34. Fry DE, Pine M, Nedza SM, Locke DG, Reband AM, Pine G

(2017) Risk-adjusted hospital outcomes in Medicare total joint

replacement surgical procedures. J Bone Jt Surg Am 99(1):10–18

35. Geraedts M, Cruppe WD, Blum K, Ohmann C (2008) Imple-

mentation and effects of Germany’s minimum volume regula-

tions results of the accompanying research. Dtsch Arztebl Int

105(51–52):890–896

36. Geraedts M, Cruppé Wd, Blum K, Ohmann C (2008) Imple-

mentation and effects of Germany’s minimum volume regula-

tions—results of the accompanying research. Dtsch Arztebl Int

105(51–52):890–896

37. Goossen K, Rombey T, Kugler CM, De Santis KK, Pieper D

(2021) Author queries via email text elicited high response and

took less reviewer time than data forms - a randomised study

within a review. J Clin Epidemiol 135:1–9. https:// doi. org/ 10.

1016/j. jclin epi. 2021. 02. 006

38. Greenland S, Longnecker MP (1992) Methods for trend estima-

tion from summarized dose-response data, with applications to

meta-analysis. Am J Epidemiol 135(11):1301–1309

39. Grouven U, Kuchenhoff H, Schrader P, Bender R (2008) Flex-

ible regression models are useful tools to calculate and assess

threshold values in the context of minimum provider volumes. J

Clin Epidemiol 61(11):1125–1131

40. Gutierrez B, Culler SD, Freund DA (1998) Does hospital proce-

dure-specific volume affect treatment costs? A national study of

knee replacement surgery. Health Serv Res 33(3 Pt 1):489–511

41. Guyatt GH, Oxman AD, Santesso N, Helfand M, Vist G, Kunz R

etal (2013) Grade guidelines: 12. Preparing summary of findings

tables-binary outcomes. J Clin Epidemiol 66(2):158–172

42. Heck DA, Robinson RL, Partridge CM, Lubitz RM, Freund DA

(1998) Patient outcomes after knee replacement. Clin Orthop

Relat Res 356:93–110

43. Hentschker C, Mennicken R, Reifferscheid A, Thomas D, Wasem

J, Wübker A (2016) Der kausale zusammenhang zwischen zahl

der fälle und behandlungsqualität in der krankenhausversorgung

(rwi materialien heft 101). Rheinisch-Westfälisches Institut für

Wirtschaftsforschung, Essen (Germany). http:// www. rwi- essen.

de/ publi katio nen/ rwi- mater ialien/ 377/. Accessed 07Apr 2020

44. Hentschker C, Mennicken R, Reifferscheid A, Wasem J, Wub-

ker A (2018) Volume-outcome relationship and minimum vol-

ume regulations in the german hospital sector—evidence from

nationwide administrative hospital data for the years 2005–2007.

Health Econ Rev 8(1):1–14

45. Hervey SL, Purves HR, Guller U, Toth AP, Vail TP, Pietrobon R

(2003) Provider volume of total knee arthroplasties and patient

outcomes in the hcup-nationwide inpatient sample. J Bone Jt

Surg Am 85a(9):1775–1783

46. Higgins JP, Thompson SG (2002) Quantifying heterogeneity in

a meta-analysis. Stat Med 21(11):1539–1558

47. Husted H, Hansen HC, Holm G, Bach-Dal C, Rud K, Andersen

KL etal (2006) Length of stay in total hip and knee arthroplasty

2875Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

1 3

in Danmark I: Volume, morbidity, mortality and resource utiliza-

tion. A national survey in orthopaedic departments in Denmark.

Ugeskr Laeger 168(22):2139–2143

48. Inacio MCS, Paxton EW, Graves SE, Namba RS, Nemes S

(2017) Projected increase in total knee arthroplasty in the

United States—an alternative projection model. Osteoarthr Cartil

25(11):1797–1803

49. Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen

(IQWiG) (2005) Entwicklung und anwendung von modellen zur

berechnung von schwellenwerten bei mindestmengen für die

knie-totalendoprothese. Abschlussbericht b05/01a. Stiftung für

Qualität und Wirtschaftlichkeit im Gesundheitswesen, rechts-

fähige Stiftung des bürgerlichen Rechts, Cologne (Germany).

https:// www. iqwig. de/ downl oad/ b05- 01a_ absch lussb ericht_

entwi c klung_ und_ anwen dung_ von_ model len_ zur_ berec hnung_

von_ schwe llenw erten_ bei_ minde stmen gen_ fuer_ die_ knie- total

endop rothe se. pdf? rev= 117386. Accessed 17 Feb 2021

50. Jeschke E, Citak M, Gunster C, Matthias Halder A, Heller

KD, Malzahn J etal (2017) Are TKAs performed in high-

volume hospitals less likely to undergo revision than TKAs

performed in low-volume hospitals? Clin Orthop Relat Res

475(11):2669–2674

51. Judge A, Chard J, Learmonth I, Dieppe P (2006) The effects

of surgical volumes and training centre status on outcomes fol-

lowing total joint replacement: analysis of the hospital episode

statistics for England. J Public Health (Oxf) 28(2):116–124

52. Katz JN, Barrett J, Mahomed NN, Baron JA, Wright RJ, Losina

E (2004) Association between hospital and surgeon procedure

volume and the outcomes of total knee replacement. J Bone Jt

Surg Am 86a(9):1909–1916

53. Katz JN, Bierbaum BE, Losina E (2008) Case mix and outcomes

of total knee replacement in orthopaedic specialty hospitals. Med

Care 46(5):476–480

54. Kreder HJ, Grosso P, Williams JI, Jaglal S, Axcell T, Wal EK

etal (2003) Provider volume and other predictors of outcome

after total knee arthroplasty: a population study in Ontario. Can

J Surg 46(1):15–22

55. Kugler CM, De Santis KK, Rombey T, Goossen K, Breuing J,

Könsgen N etal (2021) Perspective of potential patients on the

hospital volume-outcome relationship and the minimum volume

threshold for total knee arthroplasty: a qualitative focus group

and interview study. BMC Health Serv Res 21(1):1–17. https://

doi. org/ 10. 1186/ s12913- 021- 06641-8

56. Lau RL, Perruccio AV, Gandhi R, Mahomed NN (2012) The role

of surgeon volume on patient outcome in total knee arthroplasty:

a systematic review of the literature. BMC Musculoskelet Disord

13(1):250

57. Lee QJ, Mak WP, Wong YC (2016) Mortality following primary

total knee replacement in public hospitals in Hong Kong. Hong

Kong Med J 22(3):237–241

58. Lenguerrand E, Whitehouse MR, Beswick AD, Kunutsor SK,

Foguet P, Porter M etal (2019) Risk factors associated with revi-

sion for prosthetic joint infection following knee replacement:

an observational cohort study from England and Wales. Lancet

Infect Dis 19(6):589–600

59. Luft HS, Hunt SS, Maerki SC (1987) The volume-outcome rela-

tionship: practice-makes-perfect or selective-referral patterns?

Health Serv Res 22(2):157–182

60. Maman SR, Andreae MH, Gaber-Baylis LK, Turnbull ZA, White

RS (2019) Medicaid insurance status predicts postoperative mor-

tality after total knee arthroplasty in state inpatient databases. J

Comp Eff Res 8(14):1213–1228

61. Manley M, Ong K, Lau E, Kurtz SM (2009) Total knee arthro-

plasty survivorship in the United States Medicare population:

Effect of hospital and surgeon procedure volume. J Arthroplast

24(7):1061–1067

62. Marlow NE, Barraclough B, Collier NA, Dickinson IC, Fawcett

J, Graham JC etal (2010) Centralization and the relationship

between volume and outcome in knee arthroplasty procedures.

ANZ J Surg 80(4):234–241

63. McGowan J, Sampson M, Salzwedel DM, Cogo E, Foerster V,

Lefebvre C (2016) Press peer review of electronic search strate-

gies: 2015 guideline statement. J Clin Epidemiol 75:40–46

64. McMaster University (2020) Gradepro gdt: Gradepro guideline

development tool. Evidence Prime Inc, Hamilton. https:// grade

pro. org/. Accessed 14 Sept 2020

65. Meehan JP, Danielsen B, Kim SH, Jamali AA, White RH (2014)

Younger age is associated with a higher risk of early peripros-

thetic joint infection and aseptic mechanical failure after total

knee arthroplasty. J Bone Jt Surg Am 96A(7):529–535

66. Mesman R, Westert GP, Berden BJ, Faber MJ (2015) Why do

high-volume hospitals achieve better outcomes? A systematic

review about intermediate factors in volume-outcome relation-

ships. Health Policy 119(8):1055–1067

67. Meyer E, Weitzel-Kage D, Sohr D, Gastmeier P (2011) Impact

of department volume on surgical site infections following

arthroscopy, knee replacement or hip replacement. BMJ Qual

Saf 20(12):1069–1074

68. Mitsuyasu S, Hagihara A, Horiguchi H, Nobutomo K (2006)

Relationship between total arthroplasty case volume and patient

outcome in an acute care payment system in Japan. J Arthro-

plasty 21(5):656–663

69. Morche J, Mathes T, Pieper D (2016) Relationship between sur-

geon volume and outcomes: a systematic review of systematic

reviews. Syst Rev 5(1):204

70. Morche J, Renner D, Pietsch B, Kaiser L, Brönneke J, Gruber S

etal (2018) International comparison of minimum volume stand-

ards for hospitals. Health Policy 122(11):1165–1176

71. Muilwijk J, van den Hof S, Wille JC (2007) Associations between

surgical site infection risk and hospital operation volume and sur-

geon operation volume among hospitals in the Dutch nosocomial

infection surveillance network. Infect Control Hosp Epidemiol

28(5):557–563

72. Murad MH, Mustafa RA, Schünemann HJ, Sultan S, Santesso N

(2017) Rating the certainty in evidence in the absence of a single

estimate of effect. Evid Based Med 22(3):85–87

73. Namba RS, Cafri G, Khatod M, Inacio MC, Brox TW, Paxton

EW (2013) Risk factors for total knee arthroplasty aseptic revi-

sion. J Arthroplast 28(8 Suppl):122–127

74. Namba RS, Inacio MC, Paxton EW (2013) Risk factors associ-

ated with deep surgical site infections after primary total knee

arthroplasty: an analysis of 56,216 knees. J Bone Jt Surg Am

95(9):775–782

75. Nimptsch U, Mansky T (2017) Hospital volume and mortality

for 25 types of inpatient treatment in German hospitals: Obser-

vational study using complete national data from 2009 to 2014.

BMJ Open 7(9):19

76. Nimptsch U, Peschke D, Mansky T (2017) Minimum caseload

requirements and in-hospital mortality: observational study using

nationwide hospital discharge data from 2006 to 2013. Gesund-

heitswesen 79(10):823–834

77. Norton EC, Garfinkel SA, McQuay LJ, Heck DA, Wright JG, Dit-

tus R etal (1998) The effect of hospital volume on the in-hospital

complication rate in knee replacement patients. Health Serv Res

33(5 Pt 1):1191–1210

78. Ohmann C, Verde PE, Blum K, Fischer B, de Cruppe W, Ger-

aedts M (2010) Two short-term outcomes after instituting a

national regulation regarding minimum procedural volumes for

total knee replacement. J Bone Jt Surg Am 92(3):629–638

79. Ong KL, Lau E, Manley M, Kurtz SM (2008) Effect of proce-

dure duration on total hip arthroplasty and total knee arthroplasty

2876 Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

1 3

survivorship in the United States Medicare population. J Arthro-

plast 23(6):127–132

80. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann

TC, Mulrow CD etal (2021) The PRISMA 2020 statement: an

updated guideline for reporting systematic reviews. Syst Rev

10(1):89

81. Pamilo KJ, Peltola M, Paloneva J, Makela K, Hakkinen U,

Remes V (2015) Hospital volume affects outcome after total

knee arthroplasty. Acta Orthop 86(1):41–47

82. Pamilo KJ, Torkki P, Peltola M, Pesola M, Remes V, Paloneva

J (2018) Fast-tracking for total knee replacement reduces use of

institutional care without compromising quality. A register-based

analysis of 4 hospitals and 4256 replacements. Acta Orthop

89(2):184–189

83. Paterson JM, Williams JI, Kreder HJ, Mahomed NN, Gunraj

N, Wang X etal (2010) Provider volumes and early outcomes

of primary total joint replacement in Ontario. Can J Surg

53(3):175–183

84. Pieper D, Mathes T, Neugebauer E, Eikermann M (2013) State of

evidence on the relationship between high-volume hospitals and

outcomes in surgery: a systematic review of systematic reviews.

J Am Coll Surg 216(5):1015-1025.e1018

85. Piuzzi NS, Strnad GJ, Ali Sakr Esa W, Barsoum WK, Bloomfield

MR, Brooks PJ etal (2019) The main predictors of length of stay

after total knee arthroplasty: patient-related or procedure-related

risk factors. J Bone Jt Surg Am 101(12):1093–1101

86. Polonski A, Izbicki JR, Uzunoglu FG (2019) Centraliza-

tion of pancreatic surgery in Europe. J Gastrointest Surg

23(10):2081–2092

87. Price AJ, Alvand A, Troelsen A, Katz JN, Hooper G, Gray A etal

(2018) Knee replacement. Lancet 392(10158):1672–1682

88. Ravi B, Croxford R, Hollands S, Paterson JM, Bogoch E, Kreder

H etal (2014) Increased risk of complications following total

joint arthroplasty in patients with rheumatoid arthritis. Arthritis

Rheumatol 66(2):254–263

89. Rombey T, Goossen K, Breuing J, Mathes T, Hess S, Burchard R,

etal (2019) Hospital volume-outcome relationship in total knee

arthroplasty: a systematic review and non-linear dose-response

meta-analysis. Prospero 2019 crd42019131209. National Insti-

tute for Health Research. International prospective register of

systematic reviews, York, UK. https:// www. crd. york. ac. uk/ prosp

ero/ displ ay_ record. php? ID= CRD42 01913 1209. Accessed 19

Nov 2020

90. Rombey T, Goossen K, Breuing J, Mathes T, Hess S, Burchard R

etal (2020) Hospital volume-outcome relationship in total knee

arthroplasty: protocol for a systematic review and non-linear

dose-response meta-analysis. Syst Rev 9(1):38. https:// doi. org/

10. 1186/ s13643- 020- 01295-9

91. Santesso N, Glenton C, Dahm P, Garner P, Akl EA, Alper B etal

(2020) Grade guidelines 26: informative statements to commu-

nicate the findings of systematic reviews of interventions. J Clin

Epidemiol 119:126–135

92. Schaal T, Schoenfelder T, Klewer J, Kugler J (2017) Effects

of perceptions of care, medical advice, and hospital quality on

patient satisfaction after primary total knee replacement: a cross-

sectional study. PLoS ONE 12(6):e0178591

93. Schrader P, Grouven U, Bender R (2007) Is it possible to calcu-

late minimum provider volumes for total knee replacement using

routine data? Results of a threshold value analysis of German

quality assurance data for inpatient treatment. Der Orthopade

36(6):570–576

94. Schulze Raestrup U, Smektala R (2006) Are there relevant mini-

mum procedure volumes in trauma and orthopedic surgery? Zen-

tralbl Chir 131(6):483–492

95. Schünemann HJ, Cuello C, Akl EA, Mustafa RA, Meerpohl JJ,

Thayer K etal (2019) Grade guidelines: 18. How Robins-I and

other tools to assess risk of bias in nonrandomized studies should

be used to rate the certainty of a body of evidence. J Clin Epide-

miol 111:105–114

96. Shervin N, Rubash HE, Katz JN (2007) Orthopaedic procedure

volume and patient outcomes: a systematic literature review. Clin

Orthop Relat Res 457:35–41

97. Shin CH, Chang CB, Cho SH, Jeong JH, Kang SB (2015) Factors

associated with the incidence of revision total knee arthroplasty

in Korea between 2007 and 2012: an analysis of the National

Claim Registry. BMC Musculoskelet Disord 16(1):1–8

98. Singh JA, Kwoh CK, Boudreau RM, Lee GC, Ibrahim SA (2011)

Hospital volume and surgical outcomes after elective hip/knee

arthroplasty: a risk-adjusted analysis of a large regional database.

Arthritis Rheum 63(8):2531–2539

99. Skou ST, Roos EM, Laursen MB, Rathleff MS, Arendt-Nielsen

L, Simonsen O etal (2015) A randomized, controlled trial of

total knee replacement. N Engl J Med 373(17):1597–1606

100. Solomon DH, Chibnik LB, Losina E, Huang J, Fossel AH, Husni

E etal (2006) Development of a preliminary index that predicts

adverse events after total knee replacement. Arthritis Rheum

54(5):1536–1542

101. Song KH, Kim ES, Kim YK, Jin HY, Jeong SY, Kwak YG etal

(2012) Differences in the risk factors for surgical site infection

between total hip arthroplasty and total knee arthroplasty in the

Korean Nosocomial Infections Surveillance System (Konis).

Infect Control Hosp Epidemiol 33(11):1086–1093

102. SooHoo NF, Lieberman JR, Ko CY, Zingmond DS (2006) Fac-

tors predicting complication rates following total knee replace-

ment. J Bone Jt Surg Am 88(3):480–485

103. SooHoo NF, Zingmond DS, Lieberman JR, Ko CY (2006) Opti-

mal timeframe for reporting short-term complication rates after

total knee arthroplasty. J Arthroplast 21(5):705–711

104. Soohoo NF, Zingmond DS, Lieberman JR, Ko CY (2006) Pri-

mary total knee arthroplasty in California 1991–2001: Does hos-

pital volume affect outcomes? J Arthroplast 21(2):199–205

105. Steinbrück A, Grimberg A, Melsheimer O, Jansson V (2020)

Influence of institutional experience on results in hip and knee

total arthroplasty: an analysis from the German Arthroplasty

Registry (EPRD). Der Orthopade 49(9):808–814

106. Stengel D (2012) Auswirkungen der regelungen über mindest-

mengen. Unfallchirurg 115(9):840–843

107. Stengel D, Ekkernkamp A, Dettori J, Hanson B, Sturmer

KM, Siebert H (2004) A rapid review of the minimum quality

problems using total knee arthroplasty as an example. Where

do the magical threshold values come from? Unfallchirurg

107(10):967–988

108. Sterne JAC, Hernán MA, Reeves BC, Savović J, Berkman ND,

Viswanathan M etal (2016) Robins-i: a tool for assessing risk of

bias in non-randomised studies of interventions. BMJ 355:i4919

109. Sterne JAC, Sutton AJ, Ioannidis JPA, Terrin N, Jones DR, Lau

J etal (2011) Recommendations for examining and interpret-

ing funnel plot asymmetry in meta-analyses of randomised con-

trolled trials. BMJ 343:d4002

110. Street A, Gutacker N, Bojke C, Devlin N, Daidone S (2014)

Health services and delivery research. In: Variations in outcome

and costs among NHS providers for common surgical proce-

dures: Econometric analyses of routinely collected data. NIHR

Journals Library. Health Services and Delivery Research, South-

ampton https:// doi. org/ 10. 3310/ hsdr0 2010

111. Styron JF, Koroukian SM, Klika AK, Barsoum WK (2011)

Patient vs provider characteristics impacting hospital lengths

of stay after total knee or hip arthroplasty. J Arthroplast

26(8):1418–1426

112. Taylor HD, Dennis DA, Crane HS (1997) Relationship between

mortality rates and hospital patient volume for Medicare patients

2877Knee Surgery, Sports Traumatology, Arthroscopy (2022) 30:2862–2877

1 3

undergoing major orthopaedic surgery of the hip, knee, spine,

and femur. J Arthroplast 12(3):235–242

113. The GRADE Working Group, Schünemann H, Brożek J, Guy-

att G, Oxman A (2013) Grade handbook for grading quality of

evidence and strength of recommendations. Updated October

2013. McMaster University und Evidence Prime Inc, Hamilton.

https:// gdt. grade pro. org/ app/ handb ook/ handb ook. html. Accessed

14 Sept 2020

114. Tsai YS, Kung PT, Ku MC, Wang YH, Tsai WC (2018) Effects

of pay for performance on risk incidence of infection and of revi-

sion after total knee arthroplasty in type 2 diabetic patients: A

nationwide matched cohort study. PLoS ONE 13(11):e0206797

115. Varagunam M, Hutchings A, Black N (2015) Relationship

between patient-reported outcomes of elective surgery and hos-

pital and consultant volume. Med Care 53(4):310–316

116. Viechtbauer W (2017) The metafor package: a meta-analysis

package for R. http:// www. metaf or- proje ct. org/ doku. php.

Accessed 02 Feb 2017

117. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Van-

denbroucke JP (2007) The strengthening the reporting of obser-

vational studies in epidemiology (strobe) statement: guidelines

for reporting observational studies. PLoS Med 4(10):e296

118. Wei MH, Lin YL, Shi HY, Chiu HC (2010) Effects of provider

patient volume and comorbidity on clinical and economic out-

comes for total knee arthroplasty: a population-based study. J

Arthroplast 25(6):906-912.e901

119. Welsh RL, Graham JE, Karmarkar AM, Leland NE, Baillargeon

JG, Wild DL etal (2017) Effects of postacute settings on read-

mission rates and reasons for readmission following total knee

arthroplasty. JAMDA 18(4):367-e361

120. Wilson S, Marx RG, Pan TJ, Lyman S (2016) Meaningful thresh-

olds for the volume-outcome relationship in total knee arthro-

plasty. J Bone Jt Surg Am 98(20):1683–1690

121. Yasunaga H, Tsuchiya K, Matsuyama Y, Ohe K (2009) Analysis

of factors affecting operating time, postoperative complications,

and length of stay for total knee arthroplasty: nationwide web-

based survey. J Orthop Sci 14(1):10–16

122. Yu TH, Chou YY, Tung YC (2019) Should we pay attention to

surgeon or hospital volume in total knee arthroplasty? Evidence

from a nationwide population-based study. PLoS ONE 14(5):12

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