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Record W3041832559 · doi:10.1093/jncics/pkaa061

Should Complex Cancer Patients Requiring High-Risk Surgery Shoot for the Stars?

2020· editorial· en· W3041832559 on OpenAlexaff
Fahima Dossa, Nancy N. Baxter

Bibliographic record

VenueJNCI Cancer Spectrum · 2020
Typeeditorial
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineCancerGeneral surgerySurgeryInternal medicine

Abstract

fetched live from OpenAlex

To provide patients with understandable, accessible, hospital-quality metrics, the US Centers for Medicare and Medicaid Services (CMS) developed the Overall Hospital Quality Star Ratings. This system amalgamates data from various Hospital Compare measures, grouping measures into 7 weighted categories reflecting patient outcomes (mortality, safety, readmission), patient experience, processes of care (effectiveness, timeliness), and efficiency of care (efficient use of medical imaging). Composite scores from each category are used to generate a summary score, which is translated into a star rating from 1 to 5, reflecting hospital performance (1). CMS Star Ratings are recommended as a “starting point” to compare hospitals in nonemergency situations (2). Cancer care can be complex and surgical management carries substantial risk; the CMS Star Rating presents an opportunity for cancer patients to select hospitals where quality of care may be higher and surgical risk lower. An association between CMS Star Rating and mortality after cancer surgery has previously been demonstrated (3), suggesting if all patients selected high-ranked hospitals, postoperative mortality could be reduced. Using Medicare data for patients undergoing 1 of 5 high-risk complex cancer surgeries, Papageorge et al. (4) report higher 90-day mortality at 1-star compared with 5-star hospitals (10.4% vs 6.4%); differences were greatest for esophagectomy (19.2% vs 11%) and pancreaticoduodenectomy (17.1% vs 8.1%). The authors then modeled a scenario where all patients undergoing these 5 surgeries at 1-star hospitals were instead treated at 5-star hospitals and found this would reduce 90-day mortality from 10.4% to 6.6%. Relocation of these Medicare beneficiaries would have modest gains—84 lives saved per year—but would not have a major impact on postoperative mortality for this population. Even in a scenario where both patients treated at 1- and 2-star hospitals (30.8% of patients) were relocated to 5-star hospitals, 208 lives among the 32 591 patients treated would be saved per year. Together, these results suggest CMS Star Ratings are not particularly helpful in guiding patients, because star ratings may not account for a large degree of observed variation in postoperative deaths. To understand why, several factors should be considered. The star ratings are not specific to the surgical procedures performed; ratings are developed and applied at the hospital level and factors that go into ratings, although important, are unlikely to reflect the quality of care delivered for relatively uncommon procedures and are unlikely to be causal in the relationship between hospital and outcome. From the data presented, it is unclear how widely postoperative mortality ranged within each star group. Postoperative mortality rates will vary in these hospitals such that reliance on star ratings could lead a patient to move from a low-mortality 1-star hospital to a higher mortality 5-star hospital, and, in some jurisdictions, the best performing hospital may be a 3- or 4-star hospital. Because of how scores are generated, 5-star ratings may not equate perfectly with other hospital characteristics associated with better outcomes. Notably, CMS Star Ratings do not correlate with hospital volumes (3), a factor known to be strongly associated with surgical outcomes (5,6). Additionally, compared with lower rated hospitals, 5-star hospitals less commonly have intensive care units and larger hospitals less frequently receive 5-star ratings than smaller hospitals (7). Despite the modest benefits at the population-averaged level, individual patients may find these results convincing enough to rely on CMS Star Ratings to select hospitals for their cancer care. Migration of patients (and associated revenue) from low-ranked hospitals may compel institutions to provide higher quality care. However, there are potential downsides to such a strategy for patients and the health-care system. Although this study identifies an association between star ratings and 90-day mortality, whether postoperative outcomes are better at the 5-star hospital closest to an individual patient than the closest lower ranked hospital will vary. Additionally, major patient movement may overwhelm higher ranked hospitals, leading to longer wait times with implications for long-term cancer outcomes not offset by reduced postoperative mortality. Previous work has shown that patients with less social support, lower income, and poorer health are less willing to travel to receive care (8), and Black patients more often receive care at low-quality, higher mortality hospitals even when they live closer to high-quality hospitals than White patients (9). Encouraging patients to select hospitals based on CMS Star Ratings could, therefore, widen existing disparities (10). Though regionalization of cancer care makes practical sense for very high-risk procedures performed at low volumes, for more common procedures, greater gains may result not from bringing patients to high-quality institutions but from bringing elements of high-quality institutions to patients. Within institutions, the effects of regionalization on surgeon experience can potentially be re-created. Sahni et al. (11) have shown that operative mortality is related to a surgeon’s degree of specialization in a specific procedure (number of times the procedure was performed divided by the surgeon’s total operative volume) even after adjustment for procedure volume. Although individual surgeons may currently have low volumes of complex cancer surgeries, pooling referrals within hospitals and designating individuals to perform particular procedures can increase surgeon volumes. Additionally, previous studies have shown similar complication rates at high- and low-mortality hospitals (12,13), suggesting higher mortality arises from failure to rescue (FTR) patients who experience complications. High-volume hospitals may have lower mortality because of processes that lower FTR, such as closed intensive care units, overnight coverage, and dedicated rapid response teams (14). However, only a small degree of variation in FTR is accounted for by hospital characteristics and operative volume (15), so strategies beyond investment of resources should be considered. More generally, encouraging a safety culture and having escalation protocols can also lead to improvements in morbidity and mortality for surgical patients (16,17). Improving the quality of care for patients requiring complex cancer surgery by redirecting patients to the highest performing hospital in their region, although a simple solution, will not be achieved by using the Overall Hospital Quality Star Ratings, and such an approach may increase existing disparities. Instead, applying elements of high-quality hospitals, particularly those that require minimal resource investment, can help bridge the quality gap. Disclosures: The authors have no conflicts of interest to disclose. Role of the authors: FD drafted the manuscript. NNB provided critical review of the manuscript.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.026
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.038
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.002
Science and technology studies0.0040.002
Scholarly communication0.0080.005
Open science0.0030.002
Research integrity0.0260.035
Insufficient payload (model declined to judge)0.0140.012

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.089
GPT teacher head0.392
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2020
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