Among a contemporary cohort of patients with stage II–IV lung cancer, we found explicit medical record documentation of PS less than half the time (47%). Review of nursing and physician notes led to PS to be determinable via medical records 80% of the time. Given the central role that PS plays in clinical decision making among lung cancer patients the lack of consistent medical record documentation is troubling. When documented, we found the distribution of performance status among the cohort—34% ‘poor’ PS (when those with PS = 2 are considered ‘poor’)— to be identical to the 34% with ‘poor’ performance status reported by Lilenbaum et al in contemporary clinical studies.14
We found that ‘poor’ PS among lung cancer patients with stage III–IV disease can be predicted reasonably well regardless of whether PS = 2 is considered ‘good’ or ‘poor.’ Furthermore, this was true regardless of the level of comprehensiveness of data used, but particularly for models that used information routinely available in medical claims data or medical and pharmaceutical claims data combined where c-statistics were all above 0.70. While the inclusion of information routinely available in medical claims data marginally improved model fit and predictive accuracy when compared to a model fit using only data available in tumor registries, the inclusion of information from pharmaceutical claims data did not substantively alter model fit, regardless of how ‘good’ PS is defined.
To our knowledge this is the first study to use observational data to estimate PS for lung, or any other, cancer patients. As such, these findings represent an important contribution to the field. These findings are important to our ability to monitor quality of care and appropriateness of chemotherapy, and to our ability to prospectively identify patients who may be appropriate (but not targeted) for clinical trial or palliative care/hospice enrollment without relying on expensive and time consuming primary data collection methods. Predictive models such as the ones presented here that rely on data routinely available within large, observational databases can also be used to augment comparative effectiveness research, including comparisons of different chemotherapy regimens as well as receipt of chemotherapy vs. non-chemotherapy treatment and thereby greatly enhance the capabilities of existing electronic databases such as that available via SEER-Medicare data.
While our findings of significant differences in chemotherapy receipt by ‘good’ vs. ‘poor’ PS add face validity to the accuracy of the PS score abstracted from the medical record, the fact that 42% of patients with medical record documented ‘poor’ PS received chemotherapy in the year following diagnosis highlights the importance of attempts such as ours to make documented PS or PS proxies more readily available to those who monitor and study cancer care quality and outcomes. At the time of this study, national clinical practice guidelines for patients with non-small cell lung cancer unequivocally recommended chemotherapy for patients with PS 0 or 1. 1,3
These guidelines suggested that chemotherapy might “possibly” be of benefit in patients with PS 2, noting that those patients had been excluded from clinical trials. This was in line with expert opinions of the time21
. More recent data have shown survival and quality of life benefits for PS 2 patients, although less than with ‘good’ PS, and the most recent ASCO guidelines are more supportive of chemotherapy treatment for patients with PS 2.22
Routine lung cancer chemotherapy among patients with PS ≥ 3 continues to not be recommended by any national professional organization. Chemotherapy use in patients with little chance of benefit and more chance of toxicity may delay discussion about prognosis and dying,23,26
which may lead to further poor quality of care such as the inappropriate use of mechanical ventilation or delays in referral to hospice, worse surviving caregiver quality of life, and high end of life care costs.24
Without PS proxies, little can be done to use automated data sources to monitor and measure either the under- or over-use of chemotherapy receipt and its implications on patient and economic outcomes.
Our results should be interpreted in light of the following limitations. First, subjectivity is present in the assignment of PS. Even when assessed by a healthcare professional, PS scales are subjective in nature25
and when estimated by physicians known to be prone to error26
, usually over-estimated.14
Thus, even if our model were 100% accurate, caution would have to be used in interpreting results dependent upon an accurate classification of PS. Nonetheless, the ability to develop a useful proxy measure of PS from existing observational data will help in the use of existing national data resources such as that available with SEER-Medicare data for comparative effectiveness research. Second, our models were developed on a relatively small sample and one that is specific to one delivery system. Thus not only should care be taken when generalizing findings, but our parsimonious models may exclude important predictors of PS available in observational data. Finally, identifying patients with ‘poor’ PS by their diagnoses and use of care via claims data poses its own limitations. For instance, DME use varies significantly based on differing personal preferences and practices in addition to restrictions on reimbursement by public and private insurers. Although claims for DME offer useful information, they identify only selected people with potentially disabling conditions.27
The same is true of medical diagnoses—many of which are known to be under-captured in medical claims data—and prescription drug dispensing which reflect only those medications prescribed by physicians which the patient elected to fill. Yet, the ability to proxy PS is critical to the ability to use observational data to accurately draw conclusions about comparative effectiveness and cancer care quality at a population level if not at the bedside.
Despite these limitations, results from this study shed new light on the capacity of information routinely available in observational data to identify lung cancer patients with ‘good’ vs. ‘poor’ PS. This is especially useful for researchers interested in leveraging existing observational databases for comparative effectiveness research. Recent studies have highlighted likely overuse of chemotherapy in lung cancer treatment as well as aggressive treatment near end of life.28–30
Using a predictive model such as the one developed here with a threshold of 0.70 to proxy a patient as having ‘poor’ PS would ensure reasonably high specificity (0.69 if PS = 2 considered ‘poor’) and thereby enable identification of a population for whom the receipt of chemotherapy appears inadvisable or requiring a more tailored discussion of less benefit and more risk per current guideline recommendations, and for whom early hospice intervention maybe warranted. On the other hand, using a lower predictive threshold (0.50) and thereby increasing the sensitivity of the predictive model may be useful to health disparities researchers, where interest might be in testing a hypothesis centered on under-treatment among minority populations. Similarly, choosing a predictive threshold with a high sensitivity could facilitate population identification for observational comparative effectiveness research. The best selection of both a predictive threshold and the allocation of PS = 2 patients will ultimately depend on the user’s objectives.
Performance status has long been considered one of the strongest prognostic factors31
and is used today by clinicians to assess the appropriateness of chemotherapy treatment and regimen choice for lung cancer patients.22
With the aging population, the number of Americans with functional limitations will increase dramatically, and therefore the urgency to capture and classify functional status information will grow.32
Furthermore, given the current challenges faced by the US health care system to deliver better and more cost-effective outcomes, the importance of comparative effectiveness studies is likely to only grow. Results from our study for the first time provide health services researchers and others with a viable tool to predict PS among lung cancer patients using information routinely available in observational data. As such, the value of observational data for comparative effectiveness research and for use by those interested in understanding cancer care quality or targeting specific lung cancer patients for possible inclusion in clinical trials, hospice care or other interventions is greatly enhanced.