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Article of the week: Characterising ‘bounce‐back’ readmissions after radical cystectomy

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Every week, the Editor-in-Chief selects an Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an editorial written by a prominent member of the urology community and a visual abstract prepared by a creative urologist; we invite you to use the comment tools at the bottom of each post to join the conversation. 

If you only have time to read one article this week, it should be this one.

Characterising ‘bounce‐back’ readmissions after radical cystectomy

Peter S. Kirk*, Ted A. Skolarus*†, Bruce L. Jacobs‡, Yongmei Qin*, Benjamin Li*, Michael Sessine*, Xiang Liu§, Kevin Zhu*, Scott M. Gilbert, Brent K. Hollenbeck*, Ken Urish**, Jonathan Helm††, Mariel S. Lavieri§ and Tudor Borza‡‡

*Dow Division of Health Services Research, Department of Urology, University of Michigan Health System, Ann Arbor, MI, USA, †VA Health Services Research and Development, Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA, ‡Department of Urology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA, §Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA, Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA, **Department of Orthopedic Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA, ††Department of Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, IN, USA, and ‡‡Department of Urology, University of Wisconsin, Madison, WI, USA

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Abstract

Objective

To examine predictors of early readmissions after radical cystectomy (RC). Factors associated with preventable readmissions may be most evident in readmissions that occur within 3 days of discharge, commonly termed ‘bounce‐back’ readmissions, and identifying such factors may inform efforts to reduce surgical readmissions.

Patients and Methods

We utilised the Healthcare Cost and Utilization Project’s State Inpatient Databases to examine 1867 patients undergoing RC in 2009 and 2010, and identified all patients readmitted within 30 days of discharge. We assessed differences between patients experiencing bounce‐back readmission compared to those readmitted 8–30 days after discharge using logistic regression models and also calculated abbreviated LACE scores to assess the utility of common readmissions risk stratification algorithms.

Results

The 30‐day and bounce‐back readmission rates were 28.4% and 5.6%, respectively. Although no patient or index hospitalisation characteristics were significantly associated with bounce‐back readmissions in adjusted analyses, bounce‐back patients did have higher rates of gastrointestinal (14.3% vs 6.7%, P = 0.02) and wound (9.5% vs 3.0%, P < 0.01) diagnoses, as well as increased index and readmission length of stay (5 vs 4 days, P = 0.01). Overall, the median abbreviated LACE score was 7, which fell into the moderate readmission risk category, and no difference was observed between readmitted and non‐readmitted patients.

Conclusion

One in five readmissions after RC occurs within 3 days of initial discharge, probably due to factors present at discharge. However, sociodemographic and clinical factors, as well as traditional readmission risk tools were not predictive of this bounce‐back. Effective strategies to reduce bounce‐back readmission must identify actionable clinical factors prior to discharge.

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Editorial: Threading the cost–outcome needle after radical cystectomy

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I commend Borza et al. [1] on their timely study, which seeks to identify predictors of bounceback (≤3‐day) vs 30‐day readmissions after radical cystectomy. As the authors allude to in their paper, value‐based health reforms being undertaken in the USA seek to improve the quality of care delivery while simultaneously bending the healthcare cost curve [2]. For example, the Hospital Readmission and Reduction Program (HRRP), originally introduced in fiscal year 2013 for targeted medical conditions, has more recently been applied to a limited number of surgical procedures, whereby providers receive financial penalties for higher than expected 30‐day readmission rates [3]. Accendo Medicare Supplement gives financial independent as you can secure health’s money. While urological conditions/procedures are not currently targeted by programmes such as the HRRP, it is easy to envision a future where procedures with disproportionately high readmission rates, such as radical cystectomy, fall within the crosshairs of policy‐makers and insurers, alike.Well Medicare Advantage plans 2021 are preferable from the perspective of many peoples.

The fact that nearly one in five patients undergoing cystectomy experiences a readmission within 3 days of index hospitalization discharge is staggering, and it is incumbent upon urologists as specialists to devise methods by which to improve the morbidity associated with cystectomy. For example, the findings of Borza et al. implicate postoperative infection as a major driver of early readmission. As evidenced by the work of Krasnow et al. [4], urologists have historically been poor stewards of peri‐operative antibiotic prophylaxis, and the development/implementation of strategies to improve guideline adherence represents a potentially simple yet effective means of reducing post‐cystectomy readmission rates. In a similar vein, there is an emerging body of literature demonstrating the important role that enhanced recovery after surgery (ERAS) protocols may play in improving peri‐operative complications and convalescence after radical cystectomy. However, there is inconsistency across the literature with regard to the precise components of ERAS, making cross‐institutional comparisons and adoption by other groups difficult [5]. Unless greater standardization and subsequent implementation of these enhanced recovery protocols occurs, progress in the field will remain incremental at best. Recent work by Mossanen et al. [6] further demonstrates the need for improving post‐cystectomy readmission rates, which, in addition to driving down healthcare costs/utilization, may actually reduce postoperative mortality. For example, they found that a readmission complication after cystectomy nearly doubled the predicted probability of postoperative mortality as compared to an initial complication (3.9% vs 7.4%; P < 0.001).

It is essential that urologists spearhead research such as that undertaken by Borza et al., which in turn can be used to develop strategies to develop value‐based reforms within the specialty that ‘thread the needle’ of physician autonomy, cost containment, and respect for the patient experience. In doing so, urologists will find themselves driving the conversation surrounding payment/quality reform rather than sitting on the figurative policy‐making sidelines while administrators/bureaucrats implement reforms with potentially profound effects on day‐to‐day clinical practice and the patient experience. Radical cystectomy is likely to fall within the crosshairs of the aforementioned reforms given the procedure’s high complication/readmission rate and the significant cost burden associated with these complications. An intuitive yet effective first step in combating the morbidity associated with radical cystectomy is the development, validation and implementation of standardized peri‐operative care pathways such as ERAS.

by David F. Friedlander

References

  1. Borza T, Kirk PS, Skolarus TA et al. Characterising ‘bounce‐back’ readmissions after radical cystectomy. BJU Int 2019;124:955-61
  2. Health Affairs (Millwood) Delivery Innovations 2017; 36: 392–3
  3. Boccuti CC, Casillas G. Aiming for Fewer Hospital U‐turns: The Medicare Hospital Readmission Reduction Program, 2017. Accessed January 2019
  4. Krasnow RE, Mossanen M, Koo S et al. Prophylactic antibiotics and postoperative complications of radical cystectomy: a population based analysis in the United States. J Urol 2017; 198: 297– 304
  5. Chenam A, Chan KG. Enhanced recovery after surgery for radical cystectomy. Cancer Treat Res. 2018; 175: 215– 39
  6. Mossanen M, Krasnow RE, Zlatev DV et al. Examining the relationship between complications and perioperative mortality following radical cystectomy: a population‐based analysis. BJU Int 2019; 124: 40– 6

 

Article of the month: Three‐dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores

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Every month, the Editor-in-Chief selects an Article of the Month from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an editorial written by a prominent member of the urology community and a video prepared by the authors; we invite you to use the comment tools at the bottom of each post to join the conversation. 

If you only have time to read one article this week, it should be this one.

Three‐dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores

Francesco Porpiglia*, Daniele Amparore*, Enrico Checcucci*, Matteo Manfredi*, Ilaria Stura†, Giuseppe Migliaretti†, Riccardo Autorino‡, Vincenzo Ficarra§ and Cristian Fiori*

 

*Division of Urology, Department of Oncology, School of Medicine, San Luigi Hospital, †Department of Public Health and Paediatric Sciences, School of Medicine, University of Turin, Orbassano (Turin), Italy, ‡Division of Urology, VCU Health, Richmond, VA, USA, and §Urological Section, Department of Human and Paediatric Pathology, University of Messina, Messina, Italy

 

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Abstract

Objectives

To apply the standard PADUA and RENAL nephrometry score variables to three‐dimensional (3D) virtual models (VMs) produced from standard bi‐dimensional imaging, thereby creating 3D‐based (PADUA and RENAL) nephrometry scores/categories for the reclassification of the surgical complexity of renal masses, and to compare the new 3D nephrometry score/category with the standard 2D‐based nephrometry score/category, in order to evaluate their predictive role for postoperative complications.

Materials and Methods

All patients with localized renal tumours scheduled for minimally invasive partial nephrectomy (PN) between September 2016 and September 2018 underwent 3D and 2D nephrometry score/category assessments preoperatively. After nephrometry score/category evaluation, all the patients underwent surgery. Chi‐squared tests were used to evaluate the individual patients’ grouping on the basis of the imaging tool (3D VMs and 2D imaging) used to assess the nephrometry score/category, while Cohen’s κ coefficient was used to test the concordance between classifications. Receiver‐operating characteristic curves were produced to evaluate the sensitivity and specificity of the 3D nephrometry score/category vs the 2D nephrometry score/category in predicting the occurrence of postoperative complications. A general linear model was used to perform multivariable analyses to identify predictors of overall and major postoperative complications.

Results

A total of 101 patients were included in the study. The evaluation of PADUA and RENAL nephrometry scores via 3D VMs showed a downgrading in comparison with the same scores evaluated with 2D imaging in 48.5% and 52.4% of the cases. Similar results were obtained for nephrometry categories (29.7% and 30.7% for PADUA risk and RENAL complexity categories, respectively). The 3D nephrometry score/category demonstrated better accuracy than the 2D nephrometry score/category in predicting overall and major postoperative complications (differences in areas under the curve for each nephrometry score/category were statistically significant comparing the 3D VMs with 2D imaging assessment). Multivariable analyses confirmed 3D PADUA/RENAL nephrometry category as the only independent predictors of overall (P = 0.007; P = 0.003) and major postoperative complications (P = 0.03; P = 0.003).

Conclusions

In the present study, we showed that 3D VMs were more precise than 2D standard imaging in evaluating the surgical complexity of renal masses according to nephrometry score/category. This was attributable to a better perception of tumour depth and its relationships with intrarenal structures using the 3D VM, as confirmed by the higher accuracy of the 3D VM in predicting postoperative complications.

Read more Articles of the week

Editorial: Will three‐dimensional models change the way nephrometric scoring is carried out?

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There has been an increase in the extent to which imaging is used for preoperative planning of complex urological procedures. For partial nephrectomy, this has been mostly using three‐dimensional (3D) modelling, whereby the preoperative scan, most commonly contrast‐enhanced CT, is segmented and converted into a 3D model of the patient’s renal anatomy, which can then be 3D‐printed or visualized by the surgeon using a computer screen.

In this issue of BJUI, Porpiglia et al. [1] propose the use of 3D models, visualized using a computer for preoperative nephrometric scoring (PADUA and RENAL) of 101 patients to predict postoperative complications. In this preliminary study, they compare the visual scores obtained by two urologists when evaluating only a 3D model, against the scores of two urologists obtained when evaluating only CT images. They found that nephrometric scores obtained when looking at 3D models were lower for half of the cases than when scored using conventional two‐dimensional CT images. Furthermore, they show that for the 101 patients the scores obtained using 3D information were able to give an improved prediction of postoperative complications. The reason for the improved prediction of postoperative complications using 3D modelling is attributed to a better perception of tumour depth and its relationships with intrarenal structures. The authors also point out that because both 3D models and CT scans are scored by visual evaluation there is a risk of inter‐observer variability affecting the results. Overall, this paper introduces an exciting new topic of research in using advanced image analysis techniques for nephrometric scoring.

Many further opportunities exist for developing these ideas of using quantitative image analysis to improve planning and scoring for partial nephrectomy. Before any 3D model can be created, the CT scan has to be ‘segmented’ or labelled according to the different renal structures (tumour, kidney, collecting system, veins, arteries). Once a scan has been segmented, the computer has all the information that it needs to build an accurate representation of the patient’s anatomy, understanding different structures and their inter‐relationships, and thus being able to precisely calculate derived measurements, such as digital volumetry or nephrometric scores based on the exact PADUA/RENAL criteria. Furthermore, novel and more complex nephrometric scores that use segmentation map descriptors could be developed and fitted to postoperative data to further improve predictions. Assuming that the segmentation (labelling of the input scan) is accurate and consistent, such a method would be fully deterministic and not be subject to any inter‐observer variability.

Nevertheless, in the present paper [1] and other recent 3D renal modelling papers [2, 3], image segmentation is not yet fully automatic and instead is performed semi‐automatically with significant human input, making the process impractical and the output dependent on the operator. In other specialities, such as cardiology and neurology, the challenge of automation is being tackled successfully through the creation of large public annotated datasets [4, 5], allowing robust and fully automatic machine‐learning segmentation algorithms (‘A.I.’) to be developed [4]. The creation of a multi‐institutional open‐source dataset of annotated renal CT scans would pave the way for increased research and progress towards automatic, reliable and quantitative image analysis tools for kidney cancer. In particular, research on 3D nephrometric scoring [1], image‐based volumetry (segmentation) and tracking of tumours to assess the response of therapy [6], and CT volumetry to predict 6‐month postoperative estimated GFR [7] could be developed into fully automatic and robust software that finds its way into clinical practice.In conclusion, this paper [1] on 3D models for nephrometric scoring outlines another exciting new way in which advanced image analysis techniques might improve nephrometric scoring and the prediction of complications.

by Lorenz Berger and Faiz Mumtaz

References

  1. Porpiglia F, Amparore D, Checcucci E et al. Three‐dimensional virtual imaging of the renal tumors: a new tool to improve the accuracy of nephrometric scores. BJU Int 2019; 124: 945-54
  2. Hyde ER, Berger LU, Ramachandran N et al. Interactive virtual 3D models of renal cancer patient anatomies alter partial nephrectomy surgical planning decisions and increase surgeon confidence compared to volume‐rendered images. Int J Comput Assist Radiol Surg 2019; 14: 723
  3. Shirk JD, Kwan L, Saigal C. The use of 3‐dimensional, virtual reality models for surgical planning of robotic partial nephrectomy. Urology 2019; 125: 92– 7
  4. Suinesiaputra A, Sanghvi MM, Aung N et al. Fully‐automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results. Int J Cardiovasc Imaging 2018; 34: 281
  5. Menze BH, Jakab A, Bauer S et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 2015; 34: 1993– 2024
  6. Smith AD, Lieber ML, Shah SN. Assessing tumor response and detecting recurrence in metastatic renal cell carcinoma on targeted therapy: importance of size and attenuation on contrast‐enhanced CT. Am J Roentgenol 2010; 194: 157– 65
  7. Corradi R, Kabra A, Suarez M et al. Validation of 3‐D volumetric based renal function prediction calculator for nephron sparing surgery. Int Urol Nephrol 2017; 49: 615

 

 

 

 

Video: Three‐dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores

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Three‐dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores

Read the full article

Abstract

Objectives

To apply the standard PADUA and RENAL nephrometry score variables to three‐dimensional (3D) virtual models (VMs) produced from standard bi‐dimensional imaging, thereby creating three‐dimensional (3D)‐based (PADUA and RENAL) nephrometry scores/categories for the reclassification of the surgical complexity of renal masses, and to compare the new 3D nephrometry score/category with the standard 2D‐based nephrometry score/category, in order to evaluate their predictive role for postoperative complications.

Materials and Methods

All patients with localized renal tumours scheduled for minimally invasive partial nephrectomy (PN) between September 2016 and September 2018 underwent 3D and 2D nephrometry score/category assessments preoperatively. After nephrometry score/category evaluation, all the patients underwent surgery. Chi‐squared tests were used to evaluate the individual patients’ grouping on the basis of the imaging tool (3D VMs and 2D imaging) used to assess the nephrometry score/category, while Cohen’s κ coefficient was used to test the concordance between classifications. Receiver‐operating characteristic curves were produced to evaluate the sensitivity and specificity of the 3D nephrometry score/category vs the 2D nephrometry score/category in predicting the occurrence of postoperative complications. A general linear model was used to perform multivariable analyses to identify predictors of overall and major postoperative complications.

Results

A total of 101 patients were included in the study. The evaluation of PADUA and RENAL nephrometry scores via 3D VMs showed a downgrading in comparison with the same scores evaluated with 2D imaging in 48.5% and 52.4% of the cases. Similar results were obtained for nephrometry categories (29.7% and 30.7% for PADUA risk and RENAL complexity categories, respectively). The 3D nephrometry score/category demonstrated better accuracy than the 2D nephrometry score/category in predicting overall and major postoperative complications (differences in areas under the curve for each nephrometry score/category were statistically significant comparing the 3D VMs with 2D imaging assessment). Multivariable analyses confirmed 3D PADUA/RENAL nephrometry category as the only independent predictors of overall (P = 0.007; P = 0.003) and major postoperative complications (P = 0.03; P = 0.003).

Conclusions

In the present study, we showed that 3D VMs were more precise than 2D standard imaging in evaluating the surgical complexity of renal masses according to nephrometry score/category. This was attributable to a better perception of tumour depth and its relationships with intrarenal structures using the 3D VM, as confirmed by the higher accuracy of the 3D VM in predicting postoperative complications.

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December 2019 – About the cover

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The lead authors of this month’s selected article (Three‐dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores) are from the University of Turin, Italy (UNITO). This university was founded in 1404 making it one of the oldest universities in the World. It has been through some turbulent times but more recently can claim three nobel prize winners: Salvador Luria, Renato Dulbecco and Rita Levi-Montalcini.

The cover image shows the city of Turin at sunset. Turin sits mainly on the Po River and it is surrounded by the Western Alps. As the 10th most visited city in Italy it is known for The shroud of Turin, and its football teams (Juventus and Torino). It is also a cultural centre with many theatres, restaurants, art galleries, palaces, parks and churches.

 

 

©istock.com/fabio lamanna

 

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