Developing a comorbidity score in cancer patients using healthcare utilization databases during the COVID-19 pandemic: An experience from Italy.

BACKGROUND: A strong relationship has been observed between comorbidities and the risk of severe/fatal COVID-19 manifestations, but no score is available to evaluate their association in cancer patients. To make up for this lacuna, we aimed to develop a comorbidity score for cancer patients, based on the Lombardy Region healthcare databases. METHODS: We used hospital discharge records to identify patients with a new diagnosis of solid cancer between February and December 2019; 61 comorbidities were retrieved within 2 years before cancer diagnosis. This cohort was split into training and validation sets. In the training set, we used a LASSO-logistic model to identify comorbidities associated with the risk of developing a severe/fatal form of COVID-19 during the first pandemic wave (March-May 2020). We used a logistic model to estimate comorbidity score weights and then we divided the score into five classes (<=-1, 0, 1, 2-4, >=5). In the validation set, we assessed score performance by areas under the receiver operating characteristic curve (AUC) and calibration plots. We repeated the process on second pandemic wave (October-December 2020) data. RESULTS: We identified 55,425 patients with an incident solid cancer. We selected 21 comorbidities as independent predictors. The first four score classes showed similar probability of experiencing the outcome (0.2% to 0.5%), while the last showed a probability equal to 5.8%. The score performed well in both the first and second pandemic waves: AUC 0.85 and 0.82, respectively. Our results were robust for major cancer sites too (i.e., colorectal, lung, female breast, and prostate). CONCLUSIONS: We developed a high performance comorbidity score for cancer patients and COVID-19. Being based on administrative databases, this score will be useful for adjusting for comorbidity confounding in epidemiological studies on COVID-19 and cancer impact.

Research strategies in treatment of hypertension: value of retrospective real-life data.

This review will discuss the limitations of data collected by RCTs in relation to their applicability to daily life clinical management. It will then argue that these limitations are only partially overcome by modifications of RCT design and conduction (e.g. 'pragmatic trials') while being substantially attenuated by real-life-derived research, which can fill many gaps left by trial-collected evidence and have thus an important complementary value. The focus will be on the real-life research approach based on the retrospective analysis of the now widely available healthcare utilization databases (formerly known as administrative databases), which will be discussed in detail for their multiple advantages as well as challenges. Emphasis will be given to the potential of these databases to provide low-cost information over long periods on many different healthcare issues, drug therapies in particular, from the general population to clinically important subgroups, including (i) prognostic aspects of treatments implemented at the medical practice level via hospitalization and fatality data and (ii) medical practice-related phenomena such as low treatment adherence and therapeutic inertia (unsatisfactorily evaluated by RCTs). It will also be mentioned that thanks to the current availability of these data in electronic format, results can be obtained quickly, helping timely decisions under emergencies. The potential shortcomings of this approach (confounding by indication, misclassification, and selection bias) will also be discussed along with their possible minimization by suitable analytic means. Finally, examples of the contributions of studies on hypertension and other cardiovascular risk factors will be offered based on retrospective healthcare utilization databases that have provided information on real-life cardiovascular treatments unavailable via RCTs.