Survival Outcomes Following the Use of Extracorporeal Membrane Oxygenation as a Rescue Technology in Critically Ill Patients: Results From Pennsylvania 2007-2015.

OBJECTIVE: To examine real-world outcomes of survival, length of stay, and discharge destination, among all adult extracorporeal membrane oxygenation admissions in one state over nearly a decade. DESIGN: Retrospective analysis of administrative discharge data. SETTING: State-wide administrative discharge data from Pennsylvania between 2007 and 2015. PATIENTS: All 2,948 consecutive patients billed under a Diagnosis-Related Grouper 3 grouper and in whom a procedural code for extracorporeal membrane oxygenation was present, admitted between the beginning of 2007 and the end of 2015 to hospitals regulated by the state of Pennsylvania. Admitting diagnoses were coded as respiratory, cardiac, cardiac arrest, or uncategorized based on administrative data. MEASUREMENTS AND MAIN RESULTS: Unadjusted in-hospital mortality, length of stay, and discharge destination. Summary statistics and tests of differences by age 65 years or older and by admitting diagnosis were performed. Outcomes by age were plotted using running-mean smoothed graphs. Over the 9-year period, the average observed death rate was 51.7%. Among all survivors, 14.6% went home to self-care and a further 15.2% to home health care. Of all survivors, 43.8% were readmitted within 1 month, and 60.6% within 1 year. Among elderly survivors, readmission rates were 52.3% and 65.5% within 1 month and 1 year, respectively. The likelihood of dying in-hospital increased with age that of being discharged home or to postacute care decreased. CONCLUSIONS: In a "usual clinical practice" setting, short-term outcomes are similar to those observed in clinical trials such as Conventional Ventilation or ECMO for Severe Adult Respiratory Failure, in registries such as extracorporeal life support organization, and in smaller single-site studies. More data on longer term follow-up are needed to allow clinicians to better inform patient selection and care.

Frequencies of Private Mentions and Sharing of Mammography and Breast Cancer Terms on Facebook: A Pilot Study.

BACKGROUND: The most popular social networking site in the United States is Facebook, an online forum where circles of friends create, share, and interact with each other's content in a nonpublic way. OBJECTIVE: Our objectives were to understand (1) the most commonly used terms and phrases relating to breast cancer screening, (2) the most commonly shared website links that other women interacted with, and (3) the most commonly shared website links, by age groups. METHODS: We used a novel proprietary tool from Facebook to analyze all of the more than 1.7 million unique interactions (comments on stories, reshares, and emoji reactions) and stories associated with breast cancer screening keywords that were generated by more than 1.1 million unique female Facebook users over the 1 month between November 15 and December 15, 2016. We report frequency distributions of the most popular shared Web content by age group and keywords. RESULTS: On average, each of 59,000 unique stories during the month was reshared 1.5 times, commented on nearly 8 times, and reacted to more than 20 times by other users. Posted stories were most often authored by women aged 45-54 years. Users shared, reshared, commented on, and reacted to website links predominantly to e-commerce sites (12,200/1.7 million, 36% of all the most popular links), celebrity news (n=8800, 26%), and major advocacy organizations (n=4900, 15%; almost all accounted for by the American Cancer Society breast cancer site). CONCLUSIONS: On Facebook, women shared and reacted to links to commercial and informative websites regarding breast cancer and screening. This information could inform patient outreach regarding breast cancer screening, indirectly through better understanding of key issues, and directly through understanding avenues for paid messaging to women authoring and reacting to content in this space.

Factors associated with increased cesarean risk among African American women: evidence from California, 2010.

OBJECTIVES: We studied if both observed and unobserved maternal health in African American women in hospitals or communities were associated with cesarean delivery of infants. METHODS: We examined the relationship between African American race and cesarean delivery among 493 433 women discharged from 255 Californian hospitals in 2010 using administrative data; we adjusted for patient comorbidities and maternal, fetal, and placental risk factors, as well as clustering of patients within hospitals. RESULTS: Cesarean rates were significantly higher overall for African American women than other women (unadjusted rate 36.8% vs 32.7%), as were both elective and emergency primary cesarean rates. Elevated risks persisted after risk adjustment (odds ratio generally > 1.27), but the prevalence of particular risk factors varied. Although African American women were clustered in some hospitals, the proportion of African Americans among all women delivering in a hospital was not related to its overall cesarean rate. CONCLUSIONS: To address the higher likelihood of elective cesarean delivery, attention needs to be given to currently unmeasured patient-level health factors, to the quality of provider-physician interactions, as well as to patient preferences.

Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients: validation and comparison to existing models.

BACKGROUND: There is increasing interest in using prediction models to identify patients at risk of readmission or death after hospital discharge, but existing models have significant limitations. Electronic medical record (EMR) based models that can be used to predict risk on multiple disease conditions among a wide range of patient demographics early in the hospitalization are needed. The objective of this study was to evaluate the degree to which EMR-based risk models for 30-day readmission or mortality accurately identify high risk patients and to compare these models with published claims-based models. METHODS: Data were analyzed from all consecutive adult patients admitted to internal medicine services at 7 large hospitals belonging to 3 health systems in Dallas/Fort Worth between November 2009 and October 2010 and split randomly into derivation and validation cohorts. Performance of the model was evaluated against the Canadian LACE mortality or readmission model and the Centers for Medicare and Medicaid Services (CMS) Hospital Wide Readmission model. RESULTS: Among the 39,604 adults hospitalized for a broad range of medical reasons, 2.8% of patients died, 12.7% were readmitted, and 14.7% were readmitted or died within 30 days after discharge. The electronic multicondition models for the composite outcome of 30-day mortality or readmission had good discrimination using data available within 24 h of admission (C statistic 0.69; 95% CI, 0.68-0.70), or at discharge (0.71; 95% CI, 0.70-0.72), and were significantly better than the LACE model (0.65; 95% CI, 0.64-0.66; P =0.02) with significant NRI (0.16) and IDI (0.039, 95% CI, 0.035-0.044). The electronic multicondition model for 30-day readmission alone had good discrimination using data available within 24 h of admission (C statistic 0.66; 95% CI, 0.65-0.67) or at discharge (0.68; 95% CI, 0.67-0.69), and performed significantly better than the CMS model (0.61; 95% CI, 0.59-0.62; P < 0.01) with significant NRI (0.20) and IDI (0.037, 95% CI, 0.033-0.041). CONCLUSIONS: A new electronic multicondition model based on information derived from the EMR predicted mortality and readmission at 30 days, and was superior to previously published claims-based models.

Measurement and risk adjustment of prelabor cesarean rates in a large sample of California hospitals.

OBJECTIVE: Prelabor cesareans in women without a prior cesarean is an important quality measure, yet one that is seldom tracked. We estimated patient-level risks and calculated how sensitive hospital rankings on this proposed quality metric were to risk adjustment. STUDY DESIGN: This retrospective cohort study linked Californian patient data from the Agency for Healthcare Research and Quality with hospital-level operational and financial data. Using the outcome of primary prelabor cesarean, we estimated patient-level logistic regressions in progressively more detailed models. We assessed incremental fit and discrimination, and aggregated the predicted patient-level event probabilities to construct hospital-level rankings. RESULTS: Of 408,355 deliveries by women without prior cesareans at 254 hospitals, 11.0% were prelabor cesareans. Including age, ethnicity, race, insurance, weekend and unscheduled admission, and 12 well-known patient risk factors yielded a model c-statistic of 0.83. Further maternal comorbidities, and hospital and obstetric unit characteristics only marginally improved fit. Risk adjusting hospital rankings led to a median absolute change in rank of 44 places compared to rankings based on observed rates. Of the 48 (49) hospitals identified as in the best (worst) quintile on observed rates, only 23 (18) were so identified by the risk-adjusted model. CONCLUSION: Models predict primary prelabor cesareans with good discrimination. Systematic hospital-level variation in patient risk factors requires risk adjustment to avoid considerably different classification of hospitals by outcome performance. An opportunity exists to define this metric and report such risk-adjusted outcomes to stakeholders.

Long-term impact of Medicare managed care on patients treated for coronary artery disease.

BACKGROUND: Planned health insurance reform promises and has started to cut reimbursement to Medicare managed care (MMC) plans. If such plans provide better care, adjusting for possible better health of their enrollees, then such reimbursement changes may have unforeseen quality consequences. OBJECTIVES: To examine whether long-term follow-up outcomes of patients who receive intensive interventional care for coronary artery disease differed by Medicare plan type. RESEARCH DESIGN: Patient-level postdischarge outcomes were multivariate adjusted logistic functions of a patient's insurance type at time of index admission. Data were retrospective secondary percutaneous coronary intervention data from Pennsylvania with 35,417 index admissions in 2004 to 2005 and in-state follow-up hospitalizations within 12 months and in-state death within 3 years of discharge. RESULTS: MMC insured patients had a consistently estimated 3-year survival benefit (relative risk of death 0.91; P value 0.003) compared with traditional Medicare traditional fee for service patients. Results were robust to propensity score stratification, subset analyses, and rich controls for observed confounders. Implausibly large associations (between an unmeasured confounder and both insurance status and outcomes) would have to be hypothesized to fully explain the observed survival benefit. CONCLUSIONS: Among a large number of Pennsylvanian elderly patients, receiving a very common therapeutic procedure for highly prevalent disease, being insured with MMC was associated with a clinically meaningful long-term survival benefit. Impending health insurance reform that changes the relative attractiveness of MMC plans may have unintended consequences on outcome quality.

Measuring the effect of policy interventions at the population level: some methodological concerns.

Health policy evaluations estimate the response of population aggregate outcomes to interventions. However, clarity on the form of the expected causal relationship, the parameter identification strategy, and the mode of hypothesis testing is required to overcome a number of conceptual and methodological problems. We use the New Jersey statewide smoking ban as an example. We examine statewide admission rates for acute myocardial infarctions, strokes and lower limb fractures, and emergency room encounter rates for asthma exacerbations before and after the smoking ban. We discuss the identification options and show the sensitivity of estimates of the response function to different specifications of the stochastic and intervention components and to different modes of inference. Model misspecification is demonstrated by rolling Chow tests for structural breaks in repeated observations.

One and done? Equality of opportunity and repeated access to scarce, indivisible medical resources.

BACKGROUND: Existing ethical guidelines recommend that, all else equal, past receipt of a medical resource (e.g. a scarce organ) should not be considered in current allocation decisions (e.g. a repeat transplantation). DISCUSSION: One stated reason for this ethical consensus is that formal theories of ethics and justice do not persuasively accept or reject repeated access to the same medical resources. Another is that restricting attention to past receipt of a particular medical resource seems arbitrary: why couldn't one just as well, it is argued, consider receipt of other goods such as income or education? In consequence, simple allocation by lottery or first-come-first-served without consideration of any past receipt is thought to best afford equal opportunity, conditional on equal medical need.There are three issues with this view that need to be addressed. First, public views and patient preferences are less ambiguous than formal theories of ethics. Empirical work shows strong preferences for fairness in health care that have not been taken into account: repeated access to resources has been perceived as unfair. Second, while difficult to consider receipt of many other prior resources including non-medical resources, this should not be used a motive for ignoring the receipt of any and all goods including the focal resource in question. Third, when all claimants to a scarce resource are equally deserving, then use of random allocation seems warranted. However, the converse is not true: mere use of a randomizer does not by itself make the merits of all claimants equal. SUMMARY: My conclusion is that not ignoring prior receipt of the same medical resource, and prioritizing those who have not previously had access to the medical resource in question, may be perceived as fairer and more equitable by society.

Why technology matters as much as science in improving healthcare.

BACKGROUND: More than half a million new items of biomedical research are generated every year and added to Medline. How successful are we at applying this steady accumulation of scientific knowledge and so improving the practice of medicine in the USA? DISCUSSION: The conventional wisdom is that the US healthcare system is plagued by serious cost, access, safety and quality weaknesses. A comprehensive solution must involve the better translation of an abundance of clinical research into improved clinical practice.Yet the application of knowledge (i.e. technology) remains far less well funded and less visible than the generation, synthesis and accumulation of knowledge (i.e. science), and the two are only weakly integrated. Worse, technology is often seen merely as an adjunct to practice, e.g. electronic health records.Several key changes are in order. A helpful first step lies in better understanding the distinction between science and technology, and their complementary strengths and limitations. The absolute level of funding for technology development must be increased as well as being more integrated with traditional science-based clinical research. In such a mission-oriented federal funding strategy, the ties between basic science research and applied research would be better emphasized and strengthened. SUMMARY: It bears repeating that only by applying the wealth of existing and future scientific knowledge can healthcare delivery and patient care ever show significant improvement.

Association between type of health insurance and elective cesarean deliveries: New Jersey, 2004-2007.

OBJECTIVES: I examined the relationship between insurance coverage, which may influence physician incentives and maternal choices, and cesarean delivery before labor. METHODS: I analyzed hospital discharge data for mothers without previous cesarean deliveries in New Jersey between 2004 and 2007, with adjustment for maternal age, race, marital status, and maternal, fetal, and placental conditions. RESULTS: Nearly 1 in 7 women (13.9%) had a cesarean delivery without laboring. Insurance status was strongly associated with cesarean birth. Women insured by Medicaid (adjusted relative risk [ARR] = 0.88; 95% confidence interval [CI] = 0.84, 0.91) or self-paying (ARR = 0.81; 95% CI = 0.78, 0.85) had a significantly lower likelihood, and women insured by BlueCross (ARR = 1.06; 95% CI = 1.03, 1.09) or standard commercial plans (ARR = 1.06; 95% CI = 1.02, 1.10) had a significantly higher likelihood of cesarean delivery than did women insured by commercial health maintenance organizations. These associations persisted in subsets restricted to lower-risk women and in qualitative sensitivity analyses for a hypothetical single, binary, unmeasured confounder. CONCLUSIONS: Insurance status has a small, independent impact on whether a woman without a previous cesarean delivery proceeds to labor or has a cesarean delivery without labor.

Forgetting the learning curve for a moment: how much performance is unrelated to own experience?

Volume-outcome relationships are of clear importance for most participants in the health-care industry; research and appropriate policy implications are of critical importance. In this letter we critique the prevailing 'learning-by-doing' view in cardiac surgery. We illustrate the very wide disparity in empirical findings on volume-outcome relationships there, in the context of broader open issues in 'learning curves' in general. Potential complementary mechanisms, e.g. 'social learning by knowledge spillovers' are introduced; these cast into doubt the prevailing policy recommendations of simple regionalization and volume smoothing.

An empirical evaluation of cusum control charts for use in robust quality control of surgical and binary outcomes.

It is well-known that standard statistical process control tools (eg, Shewhart charts) are not robust to certain features of human-generated data typically seen in health care management. For example, the presence of positive serial correlation (the tendency for successive outcomes to cluster as opposed to being truly random) leads to increased "false alarms." Previous work has introduced potential work-arounds in the case of continuous data (eg, data that can take on many values). In this article we describe a different but related problem in the case of binary data (eg, "survived" vs "deceased"). We demonstrate the value of using the Cumulative Sum chart, which is shown to be relatively robust to serial correlation, and much more efficient and effective than existing control charts.