Directing Surgical Quality Improvement Initiatives: Comparison of Perioperative Mortality and Long-Term Survival for Cancer Surgery

2008 ◽  
Vol 26 (28) ◽  
pp. 4626-4633 ◽  
Author(s):  
Karl Y. Bilimoria ◽  
David J. Bentrem ◽  
Joseph M. Feinglass ◽  
Andrew K. Stewart ◽  
David P. Winchester ◽  
...  

Purpose Quality-improvement initiatives are being developed to decrease volume-based variability in surgical outcomes. Resources for national and hospital quality-improvement initiatives are limited. It is unclear whether quality initiatives in surgical oncology should focus on factors affecting perioperative mortality or long-term survival. Our objective was to determine whether differences in hospital surgical volume have a larger effect on perioperative mortality or long-term survival using two methods. Patients and Methods From the National Cancer Data Base, 243,103 patients who underwent surgery for nonmetastatic colon, esophageal, gastric, liver, lung, pancreatic, or rectal cancer were identified. Multivariable modeling was used to evaluate 60-day mortality and 5-year conditional survival (excluding perioperative deaths) across hospital volume strata. The number of potentially avoidable perioperative and long-term deaths were calculated if outcomes at low-volume hospitals were improved to those of the highest-volume hospitals. Results Risk-adjusted perioperative mortality and long-term conditional survival worsened as hospital surgical volume decreased for all cancer sites, except for liver resections where there was no difference in survival. When comparing low- with high-volume hospitals, the hazard ratios for perioperative mortality were substantially larger than for long-term survival. However, the number of potentially avoidable deaths each year in the United States, if outcomes at low-volume hospitals were improved to the level of highest-volume centers, was significantly larger for long-term survival. Conclusion Although the magnitude of the hazard ratios implies that quality-improvement efforts should focus on perioperative mortality, a larger number of deaths could be avoided by focusing quality initiatives on factors associated with long-term survival.

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4649-4649
Author(s):  
Nicola Lehners ◽  
Natalia Becker ◽  
Axel Benner ◽  
Maria Pritsch ◽  
Elias Karl Mai ◽  
...  

Abstract Background: In the last decade, the introduction of novel agents into multiple myeloma (MM) therapy has significantly improved response rates and enabled long-term survival in a subset of patients. Yet, clinical characteristics of these long-term survivors as well as the exact impact of depth and sustainment of response still remain a matter of debate. Methods: MM patients treated at our center with high-dose melphalan supported by autologous stem cell transplantation (ASCT) as part of their first-line therapy between June 1992 and July 2014 were retrospectively analyzed. Response assessment was performed 100 days after ASCT according to EBMT criteria, since 2008 response according to IMWG criteria was also available. Overall survival (OS) and progression-free survival (PFS) were calculated from day of first ASCT. Additionally, landmark analyses regarding OS were performed at 1, 2, 3, and 5 years after ASCT. Impact of variables on PFS and OS were analyzed using multivariate Cox regression models. Furthermore, in order to assess evolution of prognosis over time, the conditional survival CS(t|s), which expresses the conditional probability of surviving further t years, was calculated as the ratio of two Kaplan-Meier estimates Ŝ(t) with . Results: 865 patients were included in this analysis, median age was 57.0 years (range 24-74), 509 were male. New agents based induction therapy was administered in 358 patients, 258 patients underwent tandem ASCT. Following ASCT, 386 patients received maintenance therapy, mainly with interferon or thalidomide. 75 patients proceeded to allogeneic transplantation and were censored at that time. Median PFS was 2.1 years, median OS was 6.4 years. Analysis of clinical influence factors revealed novel agent based induction therapy (p<0.01), maintenance therapy (p<0.01) and achievement of complete response (CR) (p=0.01) to be significantly associated with prolonged PFS, while older age (p=0.01) and thrombocytes at diagnosis < 150/nl (p=0.02) were identified as risk factors; a negative trend was seen for ISS stage 3 (p=0.067). With regard to OS, novel agent based induction therapy (p<0.01), maintenance therapy (p<0.01) and duration of time to progression (p<0.01) showed a highly significant positive impact, older age (p<0.01) and renal insufficiency at diagnosis (p=0.048) exerted a negative influence. To assess the importance of duration of response, landmark analyses were performed at 1, 2, 3, and 5 years after ASCT evaluating OS of patients with sustained CR, sustained inferior responses (non-CR), lost CR and lost non-CR at these respective time points. Remarkably, sustainment of any response showed a highly significant impact on survival at each of these time points (p<0.01) with no discernable difference between sustained CR and sustained non-CR patients. Landmark analysis at 1 year is shown in Figure 1. Administration of maintenance therapy independently improved outcome (p<0.01). Conditional survival regarding the probability to survive further three years CS(3|s) was calculated starting from the time of first ASCT stratified for the different response cohorts (see Figure 2). No significant differences could be found between patients with complete and partial response. In contrast, patients with progressive disease (PD) at day 100 after ASCT had a much lower probability of surviving the following three years after ASCT compared to patients responding to ASCT. However, those patients with PD that did survive the first year after ASCT, achieved a similar conditional three-year survival to that of patients responding initially. Conclusions: In this large retrospective study, sustainment of response after first-line ASCT was revealed as a major impact factor for OS independent of the depth of response. Administration of maintenance therapy further improved outcome, supporting the hypothesis that interventions prolonging responses achieved after ASCT are essential to reach long-term survival. Figure 1 OS of patients with sustained vs not-sustained responses at 1-year landmark analysis. Figure 1. OS of patients with sustained vs not-sustained responses at 1-year landmark analysis. Figure 2 3-year-conditional survival CS(3|s) after ASCT stratified for responses achieved. Figure 2. 3-year-conditional survival CS(3|s) after ASCT stratified for responses achieved. Figure 3 Figure 3. Disclosures Hillengass: Amgen: Consultancy, Honoraria; Celgene: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria; Novartis: Research Funding; Sanofi: Research Funding. Goldschmidt:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Chugai: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium: Membership on an entity's Board of Directors or advisory committees, Research Funding; Onyx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Raab:Amgen: Consultancy, Research Funding; BMS: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Research Funding.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2091-2091
Author(s):  
Maximilian Schinke ◽  
Inga Promny ◽  
Stefanie Hieke ◽  
Johannes M. Waldschmidt ◽  
Gabriele Ihorst ◽  
...  

Abstract Introduction: Disease monitoring based on genetics or other molecular markers obtained by noninvasive or minimally invasive methods will potentially allow the early detection of treatment response or disease progression in cancer patients. Investigations in order to identify prognostic factors, e.g. patient's baseline characteristics or molecular markers, contributing to long-term survival potentially provide important information for patients with multiple myeloma. Overall survival (OS) is not very informative for patients who already survived one or more years. To better characterize long-term survival respectively long-term survivors, conditional survival (CS) analyses are useful. Conditional survival (CS) describes probabilities of surviving t additional years given they survived s years and provides information, how prognosis evolves over time. We have demonstrated the use of CS in a large data set of multiple myeloma patients with long-term survival which is mandatory for the calculation of CS (Hieke,... Engelhardt, Schumacher. CCR 2015). Methods: We evaluated 816 consecutive multiple myeloma patients treated at our department from 1997 to 2011 with follow-up until the end of 2011. Patients' data were assessed via electronic medical record (EMR) retrieval within an innovative research data warehouse. Our platform, the University of Freiburg Translational Research Integrated Database Environment (U-RIDE), acquires and stores all patient data contained in the EMR at our hospital and provides immediate advanced text searching capacity. We assessed 21 variables including gender, age, stage and admission period. We calculated 5-years CS and stratified 5-years CS according to disease- and host-related risks. Component-wise likelihood-based boosting and variables selected by boosting were investigated in a multivariable Cox model. Results: The OS probabilities at 5- and 10- years were 50% and 25%, respectively. The 5-year CS probabilities remained almost constant over the years a patient had already survived after initial diagnosis (~50%). According to baseline variables, conditional survival estimates showed no gender differences. The estimated 5-year survival probabilities varied substantially, from 25% for patients ages 70 or older to 65% for patients younger than 60 years. Similarly, patients with D&S stage I have an estimated 5-year survival probability of about 75% compared with 40% for patients with D&S stages II and III. Significant risk factors via Cox proportional hazard model were D&S stage II+III, age >70 years, hemoglobin <10g/dl, ß2-MG ≥5.5mg/dl, LDH ≥200U/l. Renal impairment, low albumin and unfavorable cytogenetics increased the risk, but failed to reach significance. Cytogenetics, response, response duration and other risk parameters post treatment are currently included in our assessment. Of note, over the study period, admission of patients <60 years decreased from 60% to 34%, but increased for those ≥70 years from 10% to 35%, respectively, illustrating that not only young and fit, but also elderly patients are increasingly treated within large referral and university centers and that patient cohorts and risks do not remain constant over time. Conclusions: Conditional survival has attracted attention in recent years either in an absolute or relative form where the latter is based on a comparison with an age-adjusted normal population being highly relevant from a public health perspective. In its absolute form, conditional survival constitutes the quantity of major interest in a clinical context. We defined conditional survival by using the fact that the patient is alive at the prediction time s as the conditioning event. Alternatively, one could determine conditional survival, given that the patient is alive and progression-free or alive, but has progression at time s (Zamboni et al. JCO 2010). Analysis of the above and additional variables from diagnosis to prediction time s may refine conditional survival towards an even more specifically determined prognosis; follow-up response and risk parameters most likely further refining these CS analyses. Figure 1. Figure 1. Disclosures Wäsch: MSD: Research Funding; Janssen-Cilag: Research Funding; Comprehensiv Cancer Center Freiburg: Research Funding; German Cancer Aid: Research Funding.


2019 ◽  
Vol 56 (2) ◽  
pp. 271-276 ◽  
Author(s):  
Arman Kilic ◽  
Thomas G Gleason ◽  
Hiroshi Kagawa ◽  
Ahmet Kilic ◽  
Ibrahim Sultan

Abstract OBJECTIVES The aim of this study was to evaluate the impact of institutional volume on long-term outcomes following lung transplantation (LTx) in the USA. METHODS Adults undergoing LTx were identified in the United Network for Organ Sharing registry. Patients were divided into equal size tertiles according to the institutional volume. All-cause mortality following LTx was evaluated using the risk-adjusted multivariable Cox regression and the Kaplan–Meier analyses, and compared between these volume cohorts at 3 points: 90 days, 1 year (excluding 90-day deaths) and 10 years (excluding 1-year deaths). Lowess smoothing plots and receiver-operating characteristic analyses were performed to identify optimal volume thresholds associated with long-term survival. RESULTS A total of 13 370 adult LTx recipients were identified. The mean annual centre volume was 33.6 ± 20.1. After risk adjustment, low-volume centres were found to be at increased risk for 90-day mortality, [hazard ratio (HR) 1.56, P < 0.001], 1-year mortality excluding 90-day deaths (HR 1.46, P < 0.001) and 10-year mortality excluding 1-year deaths (HR 1.22, P < 0.001). These findings persisted when the centre volume was modelled as a continuous variable. The Kaplan–Meier analysis also demonstrated significant reductions in survival at each of these time points for low-volume centres (each P < 0.001). The 10-year survival conditional on 1-year survival was 37.4% in high-volume centres vs 28.0% in low-volume centres (P < 0.001). The optimal annual volume threshold for long-term survival was 26 LTx/year. CONCLUSIONS The institutional volume impacts long-term survival following LTx, even after excluding deaths within the first post-transplant year. Identifying the processes of care that lead to longer survival in high-volume centres is prudent.


2012 ◽  
Vol 116 (4) ◽  
pp. 825-834 ◽  
Author(s):  
Ole Solheim ◽  
Asgeir Store Jakola ◽  
Sasha Gulati ◽  
Tom Børge Johannesen

Object Surgical mortality is a frequent outcome measure in studies of volume-outcome relationships, and the Agency for Healthcare Research and Quality has endorsed surgical mortality after craniotomies as an Inpatient Quality Indicator. Still, the frequency and causes of 30-day mortality after neurosurgical procedures have not been much explored. The authors sought to study the frequency and possible causes of death following primary intracranial tumor operations. They also sought to explore a possible predictive value of perioperative mortality rates from neurosurgical centers in relation to long-term survival. Methods Using population-based data from the Norwegian cancer registry, the authors identified 15,918 primary operations for primary CNS tumors treated in Norway in the period from August 1955 through December 2008. Patients were followed up until death, emigration, or September 2009. Causes of mortality as indicated on death certificates were studied. Factors associated with an increased risk of perioperative death were identified. Results The overall risk of perioperative death after first-time surgery for primary intracranial tumors is currently 2.2% and has decreased over the last decades. An age ≥ 70 years and histopathological entities with poor long-term prognoses are risk factors. Overlapping lesions are also associated with excess risk, indicating that lesion size or multifocality may matter. The overall risk of perioperative death is also higher in biopsy cases than in resection cases. Perioperative mortality rates of the 4 Norwegian neurosurgical centers were not predictive of their respective long-term survival rates. Conclusions Although considered surgically related if they occur within the first 30 days of surgery, most early postoperative deaths can happen independent of the handiwork of the operating surgeon or anesthesiologist. Overall prognosis of the disease seems to be a strong predictor of perioperative death—perhaps not surprisingly since the 30-day mortality rate is merely the intonation of the Kaplan-Meier curve. Both referral and treatment policies at a neurosurgical center will therefore markedly affect such early outcomes, but early deaths may not necessarily reflect overall quality of care or long-term results. The low incidence of perioperative death in intracranial tumor surgery also greatly limits the statistical power in comparative analyses, such as between published patient series or between centers and certainly between surgeons. Therefore the authors question the value of perioperative mortality rates as a quality indicator in modern neurosurgery for tumors.


HPB ◽  
2021 ◽  
Vol 23 ◽  
pp. S685
Author(s):  
P.B. Olthof ◽  
A.-M. van Keulen ◽  
S. Buettner ◽  
J. Erdmann ◽  
B. Groot Koerkamp

2014 ◽  
Vol 51 (1) ◽  
pp. 46-52 ◽  
Author(s):  
Ângelo Zambam de MATTOS ◽  
Angelo Alves de MATTOS ◽  
Fernanda Karlinski Fernandes SACCO ◽  
Lísia HOPPE ◽  
Denise Maria Sarti de OLIVEIRA

Context Transplantation is the only cure for decompensated cirrhosis. Model for End-Stage Liver Disease (MELD) is used in liver allocation. Objectives Comparing survival of enlisted populations in pre- and post-MELD eras and estimating their long-term survival. Methods This is a retrospective study of cirrhotics enlisted for transplantation during pre- and post-MELD eras. Survival curves were generated using Kaplan-Meier’s model. Cox’s model was used to determine risk factors for mortality. Exponential, Weibull’s, normal-log and Gompertz’s models were used to estimate long-term survival. Results The study included 162 patients enlisted in pre-MELD era and 184 in post-MELD period. Kaplan-Meier’s survival curve of patients enlisted in post-MELD era was better than that of pre-MELD period (P = 0.009). This difference remained for long-term estimates, with a survival of 53.54% in 5 years and 44.64% in 10 years for patients enlisted in post-MELD era and of 43.17% and 41.75% for pre-MELD period. Era in which patients had been enlisted (P = 0.010) and MELD score at enlistment (P<0.001) were independently associated to survival with hazard ratios of 0.664 (95% CI-confidence interval = 0.487-0.906) and 1.069 (95% CI = 1.043-1.095). Conclusions MELD-based transplantation policy is superior to chronology-based one, promoting better survival for enlisted patients, even in long-term.


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