The impact of comorbidity on mortality in multiple myeloma: a Danish nationwide population‐based study

Abstract

To describe the prevalence of comorbidity and its impact on survival in newly diagnosed multiple myeloma patients compared with population controls. Cases of newly diagnosed symptomatic multiple myeloma during the 2005–2012 period were identified in the Danish National Multiple Myeloma Registry. For each myeloma patient, 10 members of the general population matched by age and sex were chosen from the national Civil Registration System. Data on comorbidity in the myeloma patients and the general population comparison cohort were collected by linkage to the Danish National Patient Registry (DNPR). Cox proportional hazards regression models were used to evaluate the prognostic significance of comorbidity. The study included 2190 cases of multiple myeloma and 21,900 population controls. The comorbidity was increased in multiple myeloma patients compared with population controls, odds ratio (OR) 1.4 (1.1–1.7). The registration of comorbidity was highly increased within the year preceding diagnosis of multiple myeloma (OR 3.0 [2.5–3.5]), which was attributable to an increased registration of various diseases, in particular, renal disease with OR 11.0 (8.1–14.9). The median follow‐up time from diagnosis of multiple myeloma for patients alive was 4.3 years (interquartile range 2.4–6.3). Patients with registered comorbidity had increased mortality compared with patients without comorbidity, hazard ratio 1.6 (1.5–1.8). Multiple myeloma patients have increased comorbidity compared with the background population, in particular during the year preceding the diagnosis of myeloma.

The characteristics, treatment patterns, and outcomes of older adults aged 80 and over with multiple myeloma

“…While these factors are more prevalent in the 80+ cohort, they do not seem to produce a disproportionate effect on survival. In the year preceding myeloma diagnosis all patients have a greater likelihood than the general population to have comorbid disease such as renal impairment, chronic pulmonary disease, congestive heart failure, and diabetes mellitus [26]. Regardless of age, the presence of these comorbid conditions warrant a specialized approach to myeloma treatment, but do not seem to merit additional concerns in patients above 80-years-old.…”

Excess cumulative incidence estimation for matched cohort survival studies

“…Table 1 summarizes the results of the simulations giving the bias and the standard error of the coefficients ( 1 , 2 ), the sampling mean of the standard error estimate, and the coverage probability of 95% confidence intervals. In Table 2, we reported the estimates of A 1 (t s ) at time points t 3 = (5, 10, 15) and t 6 = (1,2,5,10,15,25). Both tables show that the results were unbiased and the standard errors were well estimated with a sample size of 1000 exposed subjects.…”

“…Overall the cohort consisted of 5815 individuals: 985 5-year soft-tissue sarcoma survivors and 4830 matched comparison subjects. To predict the excess cumulative incidence probability of first hospital contact, three risk factors were considered in the regression model: sex, 5,3,4,5,6,8,10,15,20,25,29) where the excess cumulative incidence value was then estimated. At each of these time-points, a new outcome given by the comparison of the exposed and matched unexposed outcome was computed, together with censoring pair weights.…”

“…In this case, the outcomes under study are hospitalizations due to serious diseases, which can be precluded if the subject dies at an early stage of the follow-up. Other examples of this study design can be found in Cronin-Fenton et al, 7 Riihimäki et al, 8 Hagman et al, 9 Gregersen et al, 10 Kurtelius et al, 11 and Talibov et al 12 A distinct feature of this type of studies is that the risk for exposed subjects is naturally described on two time scales, both age and time since exposure (duration), whereas for unexposed individuals, the risk depends only on the age time scale. Although several methods exist to analyze the excess risk of a group of exposed individuals compared with the background population on the hazard scale (see, for example, Cortese et al 13 and Sasieni 14 ), none of them deals with matched cohort data.…”

Multiple Myeloma Management in COVID-19 Era

“…The median interval from the last systemic therapies to the establishment of COVID-19 diagnosis was 9 days (range: 7-19 days), and six patients received treatment that involved the bone marrow. 9 The incidence of COVID-19 was found to be higher in hospitalized patients with hematological malignancies, which accounted for 10% (95% confidence interval [CI]: [6][7][8][9][10][11][12][13][14][15][16][17] in comparison in health care providers, which accounted for 7% (95% CI: 4-12; p = 0.0322). Unfortunately, in contrast to the infected health care providers, more severe case of COVID-19 and higher mortality were found in 13 patients with hematological malignancies.…”