Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

A systematic review of the impact of volume of surgery and specialization in Norwood procedure

BMC Pediatrics201414:198

DOI: 10.1186/1471-2431-14-198

Received: 20 February 2014

Accepted: 29 July 2014

Published: 6 August 2014

Abstract

Background

The volume-outcome relationship is supposed to be stronger in high risk, low volume procedures. The aim of this systematic review is to examine the available literature on the effects of hospital and surgeon volume, specialization and regionalization on the outcomes of the Norwood procedure.

Methods

A systematic literature search was performed in Medline, Embase, and the Cochrane Library. On the basis of titles and abstracts, articles of comparative studies were obtained in full-text in case of potential relevance and assessed for eligibility according to predefined inclusion criteria. All relevant data on study design, patient characteristics, hospital volume, surgeon volume and other institutional characteristics, as well as results were extracted in standardized tables. Study selection, data extraction and critical appraisal were carried out independently by two reviewers.

Results

We included 10 studies. All but one study had an observational design. The number of analyzed patients varied from 75 to 2555. Overall, the study quality was moderate with a huge number of items with an unclear risk of bias. All studies investigating hospital volume indicated a hospital volume-outcome relationship, most of them even having significant results. The results were very heterogeneous for surgeon volume.

Conclusions

The volume-outcome relationship in the Norwood procedure can be supported. However, the magnitude of the volume effect is difficult to assess.

Keywords

Norwood procedures Hypoplastic left heart syndrome Outcome assessment (Health care) Mortality Review

Background

Previous systematic reviews (SR) have shown the presence of a significant volume-outcome relationship in surgery [16]. This relationship is supposed to be stronger in high risk, low volume procedures [710]. Two hypotheses exist for this relationship. On the one hand, a higher caseload and experience result in more effective skills (“practice makes perfect”). On the other hand, providers with better outcomes might receive more referrals increasing their volume (“selective referral”) [11, 12].

Among the termination of pregnancy, compassionate care and heart transplantation, surgical palliation is the fourth treatment option for hypoplastic left heart syndrome (HLHS). A prevalence of 0.016 to 0.025% has been reported for hypoplastic left heart syndrome in neonates [13, 14]. Infants suffering from hypoplastic left heart syndrome may undergo a three-stage reconstruction. The Norwood procedure is the first (stage 1 palliation) operation of a series of three operations. Surgical details on the surgical technique of the Norwood procedure can be found elsewhere [15, 16]. After the Norwood procedure children will generally undergo the Glenn (stage 2 palliation at 3 to 6 months of age) and Fontan procedure (stage 3 palliation at 18 to 48 months of age) [17].

The Norwood procedure is associated with high mortality rates, varying between 10 and 35% [1824]. It has been debated whether mortality rates differ by provider volume or specialization [2528]. The introduction of minimal volume standards or other interventions leading to centralized care might be of high interest for decision-makers.

Individual studies investigating quality differences between pediatric cardiac surgical centers are known to be often underpowered [29]. To the best of our knowledge, no SR on the volume-outcome relationship in the Norwood procedure exists. The aim of this systematic review is to examine the available literature on the effects of hospital and surgeon volume, specialization and regionalization on the outcomes of the Norwood procedure.

Methods

We performed a systematic literature search to identify all relevant publications on the relationship between provider volume or specialization and clinical outcomes. Medline (via PubMed), Embase (via Embase) and all databases of the Cochrane library were searched from inception to March 2013 (see Additional file 1 for search strategies). Reference lists of relevant articles were inspected to identify additional articles that could have been missed by our search strategy. No language restrictions were applied.

To be considered in this systematic review the following inclusion criteria were applied to each publication: the subject of the study was the Norwood procedure; the study had a comparative design; patient outcomes (e.g. mortality, morbidity) were studied; volume (if applicable) was defined as a distinct number (e.g. continuous variable) or a cut-off value, or specialized hospitals/units were analyzed; the study did not describe a single hospital or surgeon. All titles and abstracts were screened independently by two members of the research team and the full texts of potentially eligible articles were then obtained and further assessed for eligibility according to the review inclusion criteria. Any disagreements were resolved by discussion.

Data were extracted by one reviewer into structured summary tables and checked for accuracy by a second reviewer. Any disagreements were resolved through discussion until consensus was reached. For each publication, we extracted data on patient characteristics; setting; data source(s); study design and methodology; model adjustments; independent variable in terms of provider volume, specification or regionalization; and results.

The methodological quality of the eligible studies was assessed independently by two reviewers. Any disagreements were resolved by discussion. We modified a tool which is based on the Newcastle-Ottawa-Scale [30] that was recently used in a Cochrane review investigating the volume-outcome relationship in colorectal cancer [31]. As many of the identified studies were expected to be registry-based, we made some minor changes to the tool. We believe that the last two questions dealing with incomplete data and missing data cannot be applied to registry-based studies. For example, registries might only incorporate data on cases with complete data. Under these circumstances a question on incomplete or missing data would be pointless. Therefore, we replaced these two questions for all registry-based studies and evaluated the “quality of registry data” and the “selection of patients” instead. Both questions were previously used for a similar question related to the volume-outcome relationship in registry-based studies [32]. For all other studies we used the original assessment tool of the Cochrane review by Archampong et al. [31]. In contrast to clinical trials, all registry-based studies were assessed to have a high risk of bias in the study design item in the review. We omitted this item as it seems inappropriate to assess retrospective study designs per se to be at high risk of bias with respect to our study objective. Information in registry-based studies is obtained prospectively. There is no obvious reason why registry-based studies should be at high risk of bias due to their design. Our modified assessment tool can be found in the Additional file 2.

Because the identified studies were expected to be clinically and methodologically diverse (for example, different volume definitions), we decided a priori not to statistically combine results.

Results

Study selection and characteristics

The search strategy generated 992 hits, of which 10 studies [3341, 23, 42] (11 publications) met our inclusion criteria (see Figure 1). Additional file 3 lists the excluded studies, along with the reasons for exclusion.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2431-14-198/MediaObjects/12887_2014_Article_1129_Fig1_HTML.jpg
Figure 1

Flow chart.

One study was described in two publications [37, 38]. All but one study [41] had an observational design. Eight studies were based on registry data [33, 34, 42, 3537, 40, 38, 23], whereas two were based on clinical trial data [39, 41]. We included two studies that included a subgroup analysis for the Norwood procedure [34, 23]. All studies were performed in the US, one study additionally included patients from Canada [39]. The number of analyzed patients varied from 75 to 2555. The observation periods differed widely across studies, as well (1 to 19 years). The characteristics of the studies can be found in Table 1.
Table 1

Study characteristics

Author (pub year)

Study type

Region/country

Data source

Period

Patient characteristics

Case definition (ICD-9-CM)

Chang 2002 [34] (subgroup analysis)

Registry-based

US

NIS

1988-1997

Age ≤30 days at admission

746.7

Gutgesell [35] 2002

Registry-based

US

UHC

1990-1999

Age ≤30 days at admission

746.7 + 39.61/34.42/37.4/38.34 to 38.85/39.56/39.0

Checchia 2005 [42]

Registry-based

US

PHIS

1998-2001

Age ≤30 days at admission

746.7 + 39.0

Berry 2006 [33]

Registry-based

US

KID 1997 and 2000

1997 and 2000

Teaching vs. non-teaching:

746.5, 746.7, 747.10 + 39.0, 747.22

Age at admission in days, median (IQR): 1(0–6)/3(1–6)

Male (%) 77/60

White (%) 64/62

Medicaid (%) 32/49

Noncardiac structural anomaly (%) 5/5

Prematurity/low birth weight (%) 7/5

Aortic atresia (%) 3/2

Chromosomal anomaly (%) 3/0

Hirsch 2008 [36]

Registry-based

US

KID 2003

2003

Male 63.1%

746.7 + 39.0

White 39.6%

Non-white 33.5%

Race unknown 26.9%

Welke 2009 [23] (subgroup analysis)

Registry-based

US

STS-CHSDB

2002-2006

NR

NR

Karamlou 2010 [39]

Clinical study

US + Canada

CHSS studies

1994-2000

NR

NR

McHugh 2010 [40]

Registry-based

US

UHC

1998-2007

NR

746.7 + 34.42/39.0/35.92/37.4/(38.34 to 38.85 + 39.61)

Hornik/Pasquali 2012 [37, 38]

Registry-based

US

STS-CHSDB

2000-2009

Mean age (days) 6 (IQR 4–9)

 

Mean weight (kg) 3.18 (IQR 2.80-3.50)

Weight <2.5 kg 9.7%

Male 58.2%

Noncardiac/genetic abnormality 19.9%

Dominant ventricle: right 89.6%, left 8.0%,

TAPVR 1.3%

Mechanical ventilator support 39.9%

Mechanical circulatory support 0.8%

Shock 6.7%

Arrhythmia 2.6%

Neurologic deficit 1.3%

Complete atrioventricular block 0.2%

LOS >7 days 20.8%

Tabbutt 2012 [41]

Clinical study

US

Pediatric Heart Network SVR trial

05/2005-07/2008

NR

NR

NR not reported, KID Kids’ inpatient database, NIS Nationwide Inpatient Sample, PHIS Pediatric Health Information System, SD standard deviation, UHC University HealthSystem Consortium, STS-CHSDB Society of Thoracic Surgeons Congenital Heart Surgery Database, IQR interquartile range, TAPVR total anomalous pulmonary venous return, CHSS Congenital heart surgeons society, SVR Single Ventricle Reconstruction, y year.

Ten studies investigated the effect of hospital volume [33, 34, 42, 3537, 39, 40, 38, 41, 23]. In four of these studies the authors also investigated surgeon volume [42, 37, 39, 38, 41]. In addition to hospital volume, two studies analyzed the data by hospital type [33, 36]. Most studies employed regression models for analysis. The results of the studies are shown in Table 2.
Table 2

Study results

Author (pub year)

N analysed

Hospital type

Surgeon volume

 

Hospital volume

Model (adjustments)

Chang 2002 [34]

78 (1988–1992), 268 (1993–1997)

NI

NI

  

Hospital mortality

No model (volume treated as continuous variable; correlation coefficients)

     

1988-1992

r = -0.20 (p < 0.01)

 
     

1993-1997

r = -0.31 (p < 0.01)

 

Gutgesell 2002 [35]

1203

NI

 

NI

 

Mortality

No model

     

Low ≤ 50

50%

 
     

High >50

40%

 

Checchia 2005 [42]

801

NI

 

Survival

 

Survival

No model

   

Low ≤4

49%

Low <16

48%

 
   

High >4

69%

Medium 16-30

62%

 
     

High >30

71%

 
      

p = 0.08

 
      

Increase by 4% (95% CI 1%-7%) for every 10 additional procedures performed

Linear regression

    

Trend for mortality p = 0.13

 

Trend for mortality p = 0.02

Volume treated as continuous variable

    

Association of risk-unadjusted mortality

 

Association of risk-unadjusted mortality

 
    

r2 = NR, p = 0.312

 

r2 = 0.18, p = 0.02

 
      

Mean LOS survivors (SD)

 
     

Low <16

36.5 ± 32.4

 
     

Medium 16-30

28.7 ± 8.4

 
     

High >30

29.4 ± 5.7

 
      

p > 0.05

 
      

Mean TTD (SD)

 
     

Low <16

19.6 ± 33.2

 
     

Medium 16-30

12.2 ± 9.7

 
     

High >30

20.2 ± 9.6

 
      

p > 0.05

 
      

Median TTD (range)

 
     

Low <16

19.2 (1–104)

 
     

Medium 16-30

5.4 (1–13)

 
     

High >30

7.8 (1–27)

 
      

p > 0.05

 

Berry 2006 [33]

754 (1997), 880 (2000)

Non-teaching vs. teaching

Hospital mortality

NI

 

Hospital mortality

Logistic regression (teaching status, hospital volume, noncardiac structural anomaly, prematurity, low birth weight, aortic atresia, chromosomal anomaly)

  

OR 2.6 (1.3 - 5.3)

  

Low

OR 3.1 (1.1 – 8.3)

 
     

Mid-low

OR 2.0 (0.7 – 5.7)

 
     

Mid-high

OR 1.0 (0.5 – 1.8)

 
     

High

reference

 
     

(Categories were determined from quartiles)

  

Hirsch 2008 [36]

624

 

Hospital mortality

  

Mortality

 
  

Urban teaching

24.4%

  

Inverse association p = 0.0001

Logistic regression (volume treated as continuous variable)

  

Urban non-teach.

32.2%

    
  

Rural

34.0%

    
  

Unknown

26.6%

    

Welke 2009 [23]

1154

NI

NI

 

Mortality

 

Logistic regression (age, age-for-weight-and-sex z score, interaction between age and age-for-weight-and-sex z score, preoperative stay for more than 2 days; number of prior operations (0, 1, ≤2); renal failure or dialysis, acidosis, circulatory support or shock; preoperative ventilator support or tracheostomy; asplenia, polysplenia, or a22q11 deletion; DiGeorge syndrome; Down syndrome; procedure or procedure group; operation date

    

Low <150

OR 2.91 (1.98-4.28)

  
    

Medium 150-249

OR 1.59 (1.09-2.32)

  
    

High 250-349

OR 1.43 (1.06-1.95)

  
    

Very high ≥350

Reference

  
     

Volume categories for pediatric cardiac surgery

p = 0.002 when hospital volume analysed as continuous variable (test of no volume mortality relationship)

 

Karamlou 2010 [39]

710

NI

 

Mortality

 

Mortality

Hazard regression (Birth weight, age at operation, circulatory arrest time, ascending aortic dimension, reimplantation of the ascending aorta, shunt origin from the aorta)

    

Increased cases per year (per case): -0.004 ± 0.007 (p = 0.49) [parameter estimate ± SE]

 

Increased cases per year (per case): -0.005 ± 0.01 (p = 0.38) [parameter estimate ± SE]

 

McHugh 2010 [40]

1949

NI

NI

  

Hospital mortality

Logistic regression (?)

    

Low <20

 

OR 2.49 (1.51-4.07)

 
    

Medium 20-64

 

OR 1.75 (1.23-2.49)

 
    

High >64

 

Reference

 

Hornik/Pasquali 2012 [37, 38]

2555

NI

 

Hospital mortality

 

Hospital mortality

Logistic regression (all patient characteristics, hospital volume/surgeon volume)

   

Low ≤5

OR 1.47 (1.01-2.15

Low ≤10

OR 1.37 (0.92-2.05)

 
   

Medium 6-10

OR 1.26 (0.88-1.78)

Medium 11-20

OR 1.20 (0.80-1.82)

 
   

High >10

Reference

High >20

Reference

 
      

OR 1.17 (1.01-1.35) for a twofold decrease in hospital volume

Volume treated as continuous variable

Tabbutt 2012 [41]

549

NI

 

Renal failure

 

Renal failure

Logistic regression (anomalous pulmonary venous return, preoperative intubation, heart block, open sternum, volume)

   

Low ≤5

OR 0.31 (0.09-1.09)

Low ≤15

OR 1.55 (0.53-4.58)

 
   

Medium 6 to 10

OR 0.90 (0.28-2.91)

Medium 16 to 20

OR 0.44 (0.14-1.45)

 
   

High 11 to 15

OR 0.20 (0.06-0.61)

High 21 to 30

OR 0.32 (0.11-0.91)

 
   

Very high >15

Reference

Very high >30

Reference

 
    

Log time to first extubation in days

 

Log time to first extubation in days

Linear regression (gestational age, left atrial decompression, TR preoperatively, duration of regional cerebral perfusion, ECMO, open sternum, duration of open sternum, operations after Norwood procedure, volume)

   

Low ≤5

0.54

Low ≤15

-0.06

 
   

Medium 6 to 10

0.54

Medium 16 to 20

0.31

 
   

High 11 to 15

0.40

High 21 to 30

0.21

 
   

Very high >15

Reference

Very high >30

Reference

 
    

Log length of ventilation in days

 

Log length of ventilation in days

Linear regression (gestational age, genetic abnormality, preoperative intubation, left atrial decompression, preoperative shock, TR preoperatively, age, open sternum, operations after Norwood procedure, volume)

   

Low ≤5

0.33

Low ≤15

0.004

 
   

Medium 6 to 10

0.27

Medium 16 to 20

0.26

 
   

High 11 to 15

0.21

High 21 to 30

0.12

 
   

Very high >15

Reference

Very high >30

Referene

 
      

Log time hospital LOS in days

Linear regression (birth weight, genetic abnormality, preoperative intubation for shock, TR preoperative, duration of DHCA, operations after Norwood procedure, volume)

     

Low ≤15

0.16

 
     

Medium 16 to 20

0.34

 
     

High 21 to 30

-0.03

 
     

Very high >30

reference

 
      

Sepsis

Logistic regression (gestational age, AS/MS/VD, duration of DHCA, open sternum duration, volume)

     

Low ≤15

OR 2.28 (1.17-4.47)

 
     

Medium 16 to 20

OR 0.94 (0.40-2.19)

 
     

High 21 to 30

OR 0.64 (0.33-1.26)

 
     

Very high >30

reference

 

AS/MS/VSD aortic stenosis, mitral stenosis, ventricular septal defect, DHCA deep hypothermic circulatory arrest, ECMO extracorporeal membrane oxygenation, TR tricuspid regurgitation, LOS length of stay, SE standard error, TTD time to death, NI not investigated, NR not reported, OR odds ratio, SD standard deviation.

Study quality

Table 3 summarizes the results of the quality assessment. More than half of the items were judged to have an unclear risk of bias. Only one item in one study had a high risk of bias. Addressing incomplete data or quality of registry data was the major flaw. For this item all studies had an unclear risk of bias. Many studies had also an unclear risk of bias with respect to the representativeness of the study cohort and the comparability of the intervention and control group. All but one study had a low risk of bias with respect to the assessed outcomes.
Table 3

Study quality

Study

Representativeness of study cohort

Ascertainment of intervention

Comparability of intervention and comparison/control group

Assessment of outcomes

Addressing incomplete data/quality of registry data

Missing data on primary interventions and outcomes/selection of patients

Register based studies

      

Chang 2002 [34]

?

?

?

+

?

+

Gutgesell 2002 [35]

?

?

?

+

?

+

Checchia 2005 [42]

?

+

?

+

?

-

Berry 2006 [33]

+

+

+

+

?

+

Hirsch 2008 [36]

+

+

?

+

?

+

Welke 2009 [23]

?

+

?

+

?

?

McHugh 2010 [40]

?

?

?

+

?

+

Hornik/Pasquali 2012 [37, 38]

+

?

+

+

?

?

clinical studies

      

Karamlou 2010 [39]

?

+

?

+

?

?

Tabbutt 2012 [41]

?

+

?

?

?

?

+ Low risk of bias, - High risk of bias, ? Unclear risk of bias.

Hospital type

Berry et al. [33] found non-teaching hospitals to have a significantly higher hospital mortality (OR 2.6, 95% CI 1.3 - 5.3) when compared to teaching hospitals in a multivariate analysis based on the 1997 Kids Inpatient Database (KID). According to the authors’ analyses on the 2000 dataset (not shown) resulted in the same findings.

Hirsch et al. [36] analyzed 60 hospitals based on the Kids Inpatient Database 2003 and found the hospital mortality to be lowest in urban teaching hospitals (24.4%). This is more than 7 and 9% points lower than for urban non-teaching and rural hospitals, respectively. For more than one in four hospitals (26.6%) the type was unknown. However, these results are based on 624 Norwood procedures, 551 (88.3%) of them were performed in urban teaching hospitals.

Surgeon volume

Checchia et al. [42] analyzed the Pediatric Health Information System (PHIS) from 1998 to 2001. Surgeons with more than 4 Norwood procedures were defined as high volume and compared to their colleagues. Survival was higher in high volume surgeons (69% vs. 49%). Further analyses showed also a trend for mortality (treating surgeon volume as a continuous variable) and an association between the risk-unadjusted mortality and surgeon volume. However, all results did not reach statistical significance.

The Society of Thoracic Surgeons Congenital Heart Surgery Database (STS-CHSDB) was utilized to investigate the surgeon volume during a ten-year period [37, 38]. Low volume surgeons (≤5 procedures) had higher mortality rates when compared to high volume surgeons with more than 10 procedures (OR 1.47, 95% CI 1.01 – 2.15). Medium volume (6–10 procedures) surgeons had also higher mortality rates, but this finding was statistically not significant (OR 1.26, 95% CI 0.88-1.78).

Morbidity outcomes were investigated in the Pediatric Heart Network Single Ventricle Reconstruction (SVR) trial, running from May 2005 to July 2008 [41]. Surgeon volume was classified in four categories in intervals of five procedures. Results showed no clear volume-outcome relationship for renal failure. The chance for suffering from renal failure was highest in the highest surgeon volume group. However, findings supported a surgeon volume-outcome relationship for the time to first extubation and for the length of ventilation.

Karamlou et al. [39] did not define volume categories but treated surgeon volume solely as a continuous variable. The results of 56 surgeons who performed 710 procedures from 1994 to 2000 revealed no statistically significant relationship between surgeon volume and mortality based on the analysis of an increase of one additional case per year (p = 0.49).

Hospital volume

Hospital mortality was associated with hospital volume based on an analysis of the Kids Inpatient Database 1997 [33]. Statistical significance was only reached when low volume hospitals were compared with high volume hospitals (OR 3.1, 95% CI 1.1 – 8.3). Mid-low volume hospitals had a higher chance although statistical significance was not reached (OR 2.0, 95% CI 0.7 – 5.7), whereas mid-high volume hospitals had the same chance as high volume hospitals (OR 1.0, 95% CI 0.5 – 1.8). As already stated above, according to the authors, analyses on the 2000 dataset resulted in the same findings (not shown). Hirsch et al. [36] analyzed the Kids Inpatient Database 2003 dataset and found a highly significant hospital volume-outcome relationship based on data from 60 hospitals (p < 0.0001). A former study supports this inverse association between hospital mortality and hospital volume [34]. The correlation coefficients were r = -0.20 (p < 0.01) for the period 1988–1992 and even r = -0.31 (p < 0.01) for the next period (1993–1997).

The PHIS (data 1998 – 2001) was utilized to investigate the hospital volume-outcome relationship with three categories in intervals of 15 procedures [42]. Although there was a tendency for higher survival in high volume hospitals (high vs. medium vs. low, 71% vs. 62% vs. 48%) this turned out not to be significant (p = 0.08). Further analyses showed also a relationship for mortality (treating hospital volume as a continuous variable (p = 0.02) and an association between risk-unadjusted mortality (r2 = 0.18) and hospital volume (p = 0.02). Furthermore, the survival improved by 4% (95% CI 1-7%) for every 10 additional procedures performed. The hospital volume had no significant influence on the length of stay and the time to death (analyzed as mean and median).

McHugh et al. [40] analyzed data on 1949 Norwood procedures in 48 hospitals from the University HealthSystem Consortium (UHC) from 1998 to 2007. The hospital volume-outcome relationship was clearly supported by the findings for hospital mortality. Both low volume hospitals (OR 2.49, 95% CI 1.51-4.07) and medium volume hospitals (OR 1.75, 95% CI 1.23-2.49) had much higher mortality rates when compared with high volume hospitals (more than 30 procedures per year).

The same data source was used by Gutgesell & Gibson [35] who analyzed the period from 1990 to 1999. Their descriptive analysis of 40 hospitals showed a 10% point (50% vs. 40%) higher mortality rate in low volume hospitals (less than 50 procedures through study period) when compared to high volume hospitals.

The following decade (2000–2009) was analyzed with the STS-CHSDB [37, 38]. Low and medium hospital volume revealed higher OR (low OR 1.37, medium OR 1.20) when compared to high volume hospitals, but both results were statistically not significant. Instead, a twofold decrease in hospital volume (treated as continuous variable) resulted in a significant finding for higher hospital mortality (OR 1.17, 95% CI 1.01-1.35). The analysis by Welke et al. [23] used the same data for a five-year period (2002 to 2006) and found statistically significant effects in favor of high volume hospitals. However, volume categories were not based on Norwood procedures but on pediatric cardiac surgeries.

Karamlou et al. [39] analyzed clinical data of 29 hospitals from 1994 to 2000. When treating hospital volume as a continuous variable an increase of one additional case per year showed no statistically significant effect on the mortality rate (p = 0.38).

The Pediatric Heart Network SVR trial showed inconclusive findings with respect to the hospital volume-outcome relationship [41]. On the one hand, low volume hospitals had a higher chance for patients suffering from sepsis or renal failure. On the other hand, medium and high volume hospitals had a lower chance when compared to very high volume hospitals. The result for renal failure only proved to be statistically significant for high volume hospitals vs. very high volume hospitals (OR 0.32, 95% CI 0.11-0.91). Linear regression models also showed inconsistent findings for the time to first extubation, the length of ventilation and the length of stay.

Discussion

This article reviewed the existing literature on the volume-outcome relationship for the Norwood procedure, including specialization and regionalization. In general, these data demonstrate the presence of a volume-outcome relationship for the Norwood procedure. However, the magnitude of the volume effect is difficult to assess. It should also be kept in mind that volume is a proxy for quality and cannot fully explain differences between centers. Among other factors such as ownership, teaching status, location, size of the hospital, in particular center-specific effects might be able to explain more differences between centers [23, 43, 44]. The specialization and regionalization were studied less intensively. However, the results should be interpreted with caution for reasons outlined below.

Hospital type

The influence of the hospital type was analyzed in two studies using the Kids Inpatient Database datasets 1997 and 2003. Both studies tend to support the hypothesis of better outcomes in teaching hospitals. The definition of teaching hospitals changed from 1997 to 2000. In the data of 2001, 20.1% of the hospitals were designated as teaching hospitals, as compared to 14.3% under the definition of 1997 [45]. Furthermore, new US states were added to the Kids Inpatient Database longitudinally, resulting in a higher percentage of US population covered in 2003 than in 1997. The impact of these differences on the findings is hard to assess. Nevertheless, the Kids Inpatient Database is known to be representative for the US [45], and thus, the study results show a tendency towards lower mortality rates in teaching hospitals, although it has to be acknowledged that the majority of Norwood procedures are performed in teaching hospitals.

Hospital volume

All studies indicated a hospital volume-outcome relationship, most of them even having significant results. These data are meaningful, but each study has limitations. The single best study was done by Berry et al. [33] using the Kids Inpatient Database. They found a strong hospital volume-outcome relationship and their analysis was adjusted for a number of relevant risk factors.

Surgeon volume

Surgeon volume was much less studied. Results are very heterogeneous. Only one study reported a significant result supporting the surgeon volume-outcome relationship for mortality when comparing the highest volume category with the lowest volume category [37, 38]. The underlying STS-CHSDB is widely used for analyses in cardiac surgery [46]. We were not able to fully assess the quality of the database due to a lack of information concerning the validity and completeness. However, we are aware that the quality of the data source was described elsewhere [4648] and found to be satisfactory.

It is important to notice that the study by Tabbutt et al. is a clinical trial with inconsistent findings across volume categories. However, the findings on the time to first extubation and the length of ventilation are in congruence with our hypothesis. Although the fourth study failed marginally to reach statistical significance for mortality, the absolute risk reduction (ARR) of 20% points is highly relevant [42]. Anyhow, this finding is difficult to interpret. On the one hand, the PHIS is a highly selective sample of more than 40 hospitals (29 hospitals at the time of writing of the study) throughout the US [49]. On the other hand, it includes the largest children’s hospitals in the US. Given that these hospitals are highly specialized, we would have expected a lower absolute risk reduction.

Hospital or surgeon volume

This raises the question of whether surgeon volume might be more important than hospital volume. Multivariate models were applied in only two studies, one of them showing overall inconsistent findings with respect to our review question [41]. The second study found a statistically significant effect for surgeon volume, when adjusted for hospital volume, but not for hospital volume, when adjusted for surgeon volume [37, 38].

Strengths and weaknesses of the included studies

A number of issues that might have biased the study results should be kept in mind. First of all, taking volume as an outcome measure could be confusing as the number of performed procedures may classify the same hospital as low volume or high volume depending on the geographical area. This can make findings across studies difficult to compare.

Furthermore, surgeon and hospital volume were defined in several ways in our included studies. However, it has been shown that conclusions of hospital volume-outcome analyses are similar regardless of how hospital volume was defined [48]. There are no obvious reasons why this should differ with respect to surgeon volume. In addition, there is no specific ICD-9 or other procedure code that indicates a Norwood procedure, and therefore case definitions varied by study. However, it should be noted that the STS Congenital Heart surgery database does not rely on ICD-9 nor on other procedure codes. It contains very detailed data collection forms so that it can be concluded that the misclassification of the Norwood procedure is put to a limit compared with ICD-9 codes. Two studies used data from the STS-CHSDB [23, 37].

It has been questioned whether administrative data is as good as clinical data to explore the volume-outcome relationship [47]. This is also why we assessed the quality of registry data. Nine out of eleven studies in our review were registry-based. The first clinical study had inconsistent findings [41], while the second used inappropriate statistics [39]. So, we are not able to judge whether this might have influenced our results.

Limitations of the review

We acknowledge that our work has some limitations. First, we did not search for grey literature. This might have yielded additional information for other countries than the US, in particular. However, the Norwood procedure is rare due to their low incidence. Thus, meaningful analyses can be most expected from populous countries like the US. Nationwide samples from small countries would take much time to obtain a sufficient number of Norwood procedures for analysis. Changes in clinical practice would be likely to hamper the reliability of results. The low procedural volume is also reflected by the fact that even recently published studies are often based on much older data. In an outstanding case, the paper by Karamlou et al. [39] was published in 2010, being based on data from 1994 to 2000. The evidence presented in this systematic review is up-to-date, but our findings might not reflect the current clinical practice due to the time lag bias. Although this is a known issue in systematic reviews, the time lag bias is much more prominent in our systematic review. Furthermore, we can also not preclude the risk of overlapping databases. For instance, neonates included in the PHIS might also be included in the Kids Inpatient Database, introducing double counting of cases in our analysis (this does not affect our results for specialty centers and the hospital type).

We did not use a validated tool to appraise study quality but developed our own tool based on a previous Cochrane review, as there is no tool that can be considered the ‘gold standard’ for this kind of question. Assessing the quality of registry data was always assessed to have an unclear risk of bias due to limited information in the articles. We still believe this item to be of high relevance. As we described above, we found appropriate information in other sources than the included articles. It would require much effort to search for this information for each database. Information for older data sets might not even be available anymore. Thus, we believe it is the authors’ responsibility to provide appropriate information. However, it is not our intention to blame the authors for omitting this, as no reporting standard exists for registry-based studies.

Conclusion

In conclusion, this systematic review supports the presence of a volume-outcome relationship in the Norwood procedure. However, the magnitude of the volume effect is difficult to assess. There are significant clinical effects with respect to mortality. The question whether hospital volume or surgeon volume is a better predictor for outcomes needs more investigation. A concentration of Norwood procedures could lead to a decrease in mortality, although there is no evidence for a specific volume cut-off. Since volume is nothing more than a proxy for quality of care, additional criteria should be taken into consideration in planning concentration initiatives.

Ethics

The study did not involve any human subjects, human material, or human data.

Declarations

Acknowledgement

We thank Ms Sunya-Lee Antoine for language editing.

Funding source

There was no funding for the research or publication of this article.

Financial disclosure statement

The authors have no financial relationships relevant to this article.

Authors’ Affiliations

(1)
Institute for Research in Operative Medicine, Witten/Herdecke University
(2)
German Pediatric Heart Centre, Asklepios Clinic Sankt Augustin

References

  1. Gooiker GA, Van Gijn W, Post PN, Van De Velde CJH, Tollenaar RAEM, Wouters MWJM: A systematic review and meta-analysis of the volume-outcome relationship in the surgical treatment of breast cancer: are breast cancer patients better of with a high volume provider?. Eur J Surg Oncol. 2010, 36 (SUPPL. 1): S27-S35.View ArticlePubMedGoogle Scholar
  2. Gooiker GA, Van Gijn W, Wouters MWJM, Post PN, Van De Velde CJH, Tollenaar RAEM: Systematic review and meta-analysis of the volume-outcome relationship in pancreatic surgery. Br J Surg. 2011, 98 (4): 485-494.View ArticlePubMedGoogle Scholar
  3. Goossens-Laan CA, Gooiker GA, Van Gijn W, Post PN, Bosch JLHR, Kil PJM, Wouters MWJM: A systematic review and meta-analysis of the relationship between hospital/surgeon volume and outcome for radical cystectomy: an update for the ongoing debate. Eur Urol. 2011, 59 (5): 775-783.View ArticlePubMedGoogle Scholar
  4. Van Gijn W, Gooiker GA, Wouters MWJM, Post PN, Tollenaar RAEM, Van De Velde CJH: Volume and outcome in colorectal cancer surgery. Eur J Surg Oncol. 2010, 36 (SUPPL. 1): S55-S63.View ArticlePubMedGoogle Scholar
  5. van Heek NT, Kuhlmann KF, Scholten RJ, de Castro SM, Busch OR, van Gulik TM, Obertop H, Gouma DJ: Hospital volume and mortality after pancreatic resection: a systematic review and an evaluation of intervention in the Netherlands. Ann Surg. 2005, 242 (6): 781-788. discussion 788–790View ArticlePubMedPubMed CentralGoogle Scholar
  6. Wouters MWJM, Gooiker GA, Van Sandick JW, Tollenaar RAEM: The volume-outcome relation in the surgical treatment of esophageal cancer: a systematic review and meta-analysis. Cancer. 2012, 118 (7): 1754-1763.View ArticlePubMedGoogle Scholar
  7. Birkmeyer JD, Dimick JB, Staiger DO: Operative mortality and procedure volume as predictors of subsequent hospital performance. Ann Surg. 2006, 243 (3): 411-417. doi:10.1097/01.sla.0000201800.45264.51View ArticlePubMedPubMed CentralGoogle Scholar
  8. Birkmeyer JD, Siewers AE, Marth NJ, Goodman DC: Regionalization of high-risk surgery and implications for patient travel times. JAMA. 2003, 290 (20): 2703-2708. doi:10.1001/jama.290.20.2703View ArticlePubMedGoogle Scholar
  9. Birkmeyer JD, Siewers AE, Finlayson EV, Stukel TA, Lucas FL, Batista I, Welch HG, Wennberg DE: Hospital volume and surgical mortality in the United States. N Engl J Med. 2002, 346 (15): 1128-1137. doi:10.1056/NEJMsa012337View ArticlePubMedGoogle Scholar
  10. Pieper D, Mathes T, Neugebauer E, Eikermann M: State of evidence on the relationship between high-volume hospitals and outcomes in surgery: a systematic review of systematic reviews. J Am Coll Surg. 2013, 216 (5): 1015-1025. doi:10.1016/j.jamcollsurg.2012.12.049View ArticlePubMedGoogle Scholar
  11. Luft HS: The relation between surgical volume and mortality: an exploration of causal factors and alternative models. Med Care. 1980, 18 (9): 940-959.View ArticlePubMedGoogle Scholar
  12. Luft HS, Hunt SS, Maerki SC: The volume-outcome relationship: practice-makes-perfect or selective-referral patterns?. Health Serv Res. 1987, 22 (2): 157-182.PubMedPubMed CentralGoogle Scholar
  13. Algra SO, Breur JM, Evens FC, de Roo F, Schoof PH, Haas F: Improving surgical outcome following the Norwood procedure. Neth Heart J. 2011, 19 (9): 369-372. doi:10.1007/s12471-011-0171-8View ArticlePubMedPubMed CentralGoogle Scholar
  14. Fyler DC: Report of the New England regional infant cardiac program. Pediatrics. 1980, 65 (2 Pt 2): 375-461.Google Scholar
  15. Rodriguez E, Al-Ahmadi M, Spray TL: Surgical approach to hyploplastic left heart syndrome – Norwood stage I. Multimedia Manual of Cardio-Thoracic Surgery. 2007, 2007 (1217): doi:10.1510/mmcts.2007.002733Google Scholar
  16. Barron DJ: The Norwood procedure: in favor of the RV-PA conduit. Semin Thorac Cardiovasc Surg Pediatr Card Surg Annu. 2013, 16 (1): 52-58. doi:10.1053/j.pcsu.2013.01.002View ArticlePubMedGoogle Scholar
  17. Ohye RG, Sleeper LA, Mahony L, Newburger JW, Pearson GD, Lu M, Goldberg CS, Tabbutt S, Frommelt PC, Ghanayem NS, Laussen PC, Rhodes JF, Lewis AB, Mital S, Ravishankar C, Williams IA, Dunbar-Masterson C, Atz AM, Colan S, Minich LL, Pizarro C, Kanter KR, Jaggers J, Jacobs JP, Krawczeski CD, Pike N, McCrindle BW, Virzi L, Gaynor JW: Comparison of shunt types in the Norwood procedure for single-ventricle lesions. N Engl J Med. 2010, 362 (21): 1980-1992. doi:10.1056/NEJMoa0912461View ArticlePubMedPubMed CentralGoogle Scholar
  18. Azakie A, Martinez D, Sapru A, Fineman J, Teitel D, Karl TR: Impact of right ventricle to pulmonary artery conduit on outcome of the modified Norwood procedure. Ann Thorac Surg. 2004, 77 (5): 1727-1733. doi:10.1016/j.athoracsur.2003.10.002View ArticlePubMedGoogle Scholar
  19. Jacobs JP, O’Brien SM, Pasquali SK, Jacobs ML, Lacour-Gayet FG, Tchervenkov CI, Austin EH, Pizarro C, Pourmoghadam KK, Scholl FG, Welke KF, Mavroudis C: Variation in outcomes for benchmark operations: an analysis of the society of thoracic surgeons congenital heart surgery database. Ann Thorac Surg. 2011, 92 (6): 2184-2191. discussion 2191–2182. doi:10.1016/j.athoracsur.2011.06.008View ArticlePubMedPubMed CentralGoogle Scholar
  20. Karamichalis JM, del Nido PJ, Thiagarajan RR, Jenkins KJ, Liu H, Gauvreau K, Pigula FA, Fynn-Thompson FE, Emani SM, Mayer JE, Bacha EA: Early postoperative severity of illness predicts outcomes after the stage I Norwood procedure. Ann Thorac Surg. 2011, 92 (2): 660-665. doi:10.1016/j.athoracsur.2011.03.086View ArticlePubMedGoogle Scholar
  21. Mahle WT, Cuadrado AR, Tam VK: Early experience with a modified Norwood procedure using right ventricle to pulmonary artery conduit. Ann Thorac Surg. 2003, 76 (4): 1084-1088. discussion 1089View ArticlePubMedGoogle Scholar
  22. Tabbutt S, Dominguez TE, Ravishankar C, Marino BS, Gruber PJ, Wernovsky G, Gaynor JW, Nicolson SC, Spray TL: Outcomes after the stage I reconstruction comparing the right ventricular to pulmonary artery conduit with the modified Blalock Taussig shunt. Ann Thorac Surg. 2005, 80 (5): 1582-1590. discussion 1590–1581. doi:10.1016/j.athoracsur.2005.04.046View ArticlePubMedGoogle Scholar
  23. Welke KF, O’Brien SM, Peterson ED, Ungerleider RM, Jacobs ML, Jacobs JP: The complex relationship between pediatric cardiac surgical case volumes and mortality rates in a national clinical database. J Thorac Cardiovasc Surg. 2009, 137 (5): 1133-1140. doi:10.1016/j.jtcvs.2008.12.012View ArticlePubMedGoogle Scholar
  24. EACTS Congenital Database: http://www.eactscongenitaldb.org/db/public-reports.py?fnc=r42&dbname=database. Accessed 08/10/2013
  25. Chang RK, Klitzner TS: Resources, use, and regionalization of pediatric cardiac services. Curr Opin Cardiol. 2003, 18 (2): 98-101.View ArticlePubMedGoogle Scholar
  26. Lundstrom NR, Berggren H, Bjorkhem G, Jogi P, Sunnegardh J: Centralization of pediatric heart surgery in Sweden. Pediatr Cardiol. 2000, 21 (4): 353-357.View ArticlePubMedGoogle Scholar
  27. Pinto NM, Lasa J, Dominguez TE, Wernovsky G, Tabbutt S, Cohen MS: Regionalization in neonatal congenital heart surgery: the impact of distance on outcome after discharge. Pediatr Cardiol. 2012, 33 (2): 229-238. doi:10.1007/s00246-011-0116-4View ArticlePubMedGoogle Scholar
  28. Smith PC, Powell KR: Can regionalization decrease the number of deaths for children who undergo cardiac surgery? a theoretical analysis. Pediatrics. 2002, 110 (4): 849-850. dicsussion 849–850View ArticlePubMedGoogle Scholar
  29. Welke KF, Karamlou T, Ungerleider RM, Diggs BS: Mortality rate is not a valid indicator of quality differences between pediatric cardiac surgical programs. Ann Thorac Surg. 2010, 89 (1): 139-144. discussion 145–136. doi:10.1016/j.athoracsur.2009.08.058View ArticlePubMedGoogle Scholar
  30. Wells G, Shea B, O’Connell D, Peterson J, Welch V, Losos M, Tugwell P: The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp. Accessed 24.12.2012
  31. Archampong D, Borowski D, Wille-Jorgensen P, Iversen LH: Workload and surgeon’s specialty for outcome after colorectal cancer surgery. Cochrane Database Syst Rev. 2012, 3: CD005391-doi:10.1002/14651858.CD005391.pub3PubMedGoogle Scholar
  32. Institute for Quality and Efficiency in Health Care (IQWiG): Zusammenhang zwischen der Menge der erbrachten Leistungen und der Ergebnisqualität für die Indikation “Elektiver Eingriff Bauchaortenaneurysma”. 2006, Cologne: IQWiGGoogle Scholar
  33. Berry JG, Cowley CG, Hoff CJ, Srivastava R: In-hospital mortality for children with hypoplastic left heart syndrome after stage I surgical palliation: teaching versus nonteaching hospitals. Pediatrics. 2006, 117 (4): 1307-1313. doi:10.1542/peds.2005-1544View ArticlePubMedGoogle Scholar
  34. Chang RK, Chen AY, Klitzner TS: Clinical management of infants with hypoplastic left heart syndrome in the United States, 1988–1997. Pediatrics. 2002, 110 (2 Pt 1): 292-298.View ArticlePubMedGoogle Scholar
  35. Gutgesell HP, Gibson J: Management of hypoplastic left heart syndrome in the 1990s. Am J Cardiol. 2002, 89 (7): 842-846.View ArticlePubMedGoogle Scholar
  36. Hirsch JC, Gurney JG, Donohue JE, Gebremariam A, Bove EL, Ohye RG: Hospital mortality for Norwood and arterial switch operations as a function of institutional volume. Pediatr Cardiol. 2008, 29 (4): 713-717. doi:10.1007/s00246-007-9171-2View ArticlePubMedGoogle Scholar
  37. Hornik CP, He X, Jacobs JP, Li JS, Jaquiss RD, Jacobs ML, O’Brien SM, Welke K, Peterson ED, Pasquali SK: Relative impact of surgeon and center volume on early mortality after the Norwood operation. Ann Thorac Surg. 2012, 93 (6): 1992-1997. doi:10.1016/j.athoracsur.2012.01.107View ArticlePubMedPubMed CentralGoogle Scholar
  38. Pasquali SK, Jacobs JP, He X, Hornik CP, Jaquiss RDB, Jacobs ML, O’Brien SM, Peterson ED, Li JS: The complex relationship between center volume and outcome in patients undergoing the norwood operation. Ann Thorac Surg. 2012, 93 (5): 1556-1562.View ArticlePubMedGoogle Scholar
  39. Karamlou T, McCrindle BW, Blackstone EH, Cai S, Jonas RA, Bradley SM, Ashburn DA, Caldarone CA, Williams WG: Lesion-specific outcomes in neonates undergoing congenital heart surgery are related predominantly to patient and management factors rather than institution or surgeon experience: a congenital heart surgeons society study. J Thorac Cardiovasc Surg. 2010, 139 (3): 569-577. e561View ArticlePubMedGoogle Scholar
  40. McHugh KE, Hillman DG, Gurka MJ, Gutgesell HP: Three-stage palliation of hypoplastic left heart syndrome in the university healthSystem consortium. Congenit Heart Dis. 2010, 5 (1): 8-15.View ArticlePubMedGoogle Scholar
  41. Tabbutt S, Ghanayem N, Ravishankar C, Sleeper LA, Cooper DS, Frank DU, Lu M, Pizarro C, Frommelt P, Goldberg CS, Graham EM, Krawczeski CD, Lai WW, Lewis A, Kirsh JA, Mahony L, Ohye RG, Simsic J, Lodge AJ, Spurrier E, Stylianou M, Laussen P: Risk factors for hospital morbidity and mortality after the Norwood procedure: a report from the pediatric heart network single ventricle reconstruction trial. J Thorac Cardiovasc Surg. 2012, 144 (4): 882-895. doi:10.1016/j.jtcvs.2012.05.019View ArticlePubMedPubMed CentralGoogle Scholar
  42. Checchia PA, McCollegan J, Daher N, Kolovos N, Levy F, Markovitz B: The effect of surgical case volume on outcome after the Norwood procedure. J Thorac Cardiovasc Surg. 2005, 129 (4): 754-759. doi:10.1016/j.jtcvs.2004.07.056View ArticlePubMedGoogle Scholar
  43. Halm EA, Lee C, Chassin MR: Is volume related to outcome in health care? a systematic review and methodologic critique of the literature. Ann Intern Med. 2002, 137 (6): 511-520.View ArticlePubMedGoogle Scholar
  44. Hentschker C, Mennicken R: The volume-outcome relationship and minimum volume standards - empirical evidence for Germany. Health Econ. 2014, doi:10.1002/hec.3051Google Scholar
  45. Radley DC, Schoen C: Geographic variation in access to care–the relationship with quality. N Engl J Med. 2012, 367 (1): 3-6. doi:10.1056/NEJMp1204516View ArticlePubMedGoogle Scholar
  46. Jacobs JP, Edwards FH, Shahian DM, Haan CK, Puskas JD, Morales DL, Gammie JS, Sanchez JA, Brennan JM, O’Brien SM, Dokholyan RS, Hammill BG, Curtis LH, Peterson ED, Badhwar V, George KM, Mayer JE, Chitwood WR, Murray GF, Grover FL: Successful linking of the society of thoracic surgeons adult cardiac surgery database to centers for medicare and medicaid services medicare data. Ann Thorac Surg. 2010, 90 (4): 1150-1156. discussion 1156–1157. doi:10.1016/j.athoracsur.2010.05.042View ArticlePubMedGoogle Scholar
  47. Johnson JN, Jaggers J, Li S, O’Brien SM, Li JS, Jacobs JP, Jacobs ML, Welke KF, Peterson ED, Pasquali SK: Center variation and outcomes associated with delayed sternal closure after stage 1 palliation for hypoplastic left heart syndrome. J Thorac Cardiovasc Surg. 2010, 139 (5): 1205-1210. doi:10.1016/j.jtcvs.2009.11.029View ArticlePubMedPubMed CentralGoogle Scholar
  48. Clarke DR, Breen LS, Jacobs ML, Franklin RC, Tobota Z, Maruszewski B, Jacobs JP: Verification of data in congenital cardiac surgery. Cardiol Young. 2008, 18 (Suppl 2): 177-187. doi:10.1017/s1047951108002862View ArticlePubMedGoogle Scholar
  49. Narus SP, Srivastava R, Gouripeddi R, Livne OE, Mo P, Bickel JP, de Regt D, Hales JW, Kirkendall E, Stepanek RL, Toth J, Keren R: Federating clinical data from six pediatric hospitals: process and initial results from the PHIS + consortium. AMIA Annu Symp Proc. 2011, 2011: 994-1003.PubMedPubMed CentralGoogle Scholar
  50. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2431/14/198/prepub

Copyright

© Pieper et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Advertisement