COVID-19 Presentation and Outcomes among Cancer Patients: A Matched Case-Control Study

It has been suggested that cancer patients are at higher risk of contracting COVID-19 and at higher risk of developing a severe form of the disease and fatality. This study’s objectives were to measure the excess risk of mortality and morbidity of patients with cancer among patients hospitalized for a SARS-CoV-2 infection, and to identify factors associated with the risk of death and morbidity among cancer patients. All first cancer patients hospitalized for COVID-19 in the two main hospitals of the Lyon area were included. These patients were matched based on age, gender, and comorbidities with non-cancer control patients. A total of 108 cancer patients and 193 control patients were included. The severity at admission and the symptoms were similar between the two groups. The risk of early death was higher among cancer patients, while the risk of intubation, number of days with oxygen, length of stay in ICU, and length of hospital stay were reduced. The main factors associated with early death among cancer patients was the severity of COVID-19 and the number of previous chemotherapy lines. The outcomes appear to be driven by the severity of the infection and therapeutic limitations decided at admission

Improved 30-Day Survival Estimation in ICU Patients: A Comparative Analysis of Different Approaches With Real-World Data

International audienceObjectives: The objective of this study was to compare three different approaches for estimating 30-day survival in ICU studies, considering the issue of informative censoring that occurs when patients are lost to follow-up after discharge. Design: A comparative analysis was conducted to evaluate the effect of different approaches on the estimation of 30-day survival. Three methods were compared: the classical approach using the Kaplan-Meier (KM) estimator and Cox regression modeling, the competing risk approach using the Fine and gray model, considering censoring as a competing event, and the logistic regression approach. Setting: The study was conducted in a university ICU and data from patients admitted between 2010 and 2020 were included. Patient characteristics were collected from electronic records. Patients: A total of 10,581 patients were included in the study. The true date of death for each patient, obtained from a national registry, allowed for an absence of censoring. Interventions: All patients were censored at the time of discharge from the ICU, and the three different approaches were applied to estimate the mortality rate and the effects of covariates on mortality. Regression analyses were performed using five variables known to be associated with ICU mortality. Measurements and Main Results: The 30-day survival rate for the included patients was found to be 80.5% (95% CI, 79.7–81.2%). The KM estimator severely underestimated the 30-day survival (50.6%; 95% CI, 48.0–53.4%), while the competing risk and logistic regression approaches provided similar results, only slightly overestimating the survival rate (84.5%; 95% CI, 83.8–85.2%). Regression analyses showed that the estimates were not systematically biased, with the Cox and logistic regression models exhibiting greater bias compared with the competing risk regression method. Conclusions: The competing risk approach provides more accurate estimates of 30-day survival and is less biased compared with the other methods evaluated

Diagnostic value and relative weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers

<div><p>Purpose</p><p>To assess the diagnostic weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers (csPCa).</p><p>Materials and methods</p><p>We used a prospective database of 262 patients who underwent T2-weighted, diffusion-weighted, and dynamic contrast-enhanced (DCE) imaging before prostatectomy. For each lesion, two independent readers (R1, R2) prospectively defined nine features: shape, volume (V_Max), signal abnormality on each pulse sequence, number of pulse sequences with a marked (S_Max) and non-visible (S_Min) abnormality, likelihood of extracapsular extension (ECE) and PSA density (dPSA). Overall likelihood of malignancy was assessed using a 5-level Likert score. Features were evaluated using the area under the receiver operating characteristic curve (AUC). csPCa was defined as Gleason ≥7 cancer (csPCa-A), Gleason ≥7(4+3) cancer (csPCa-B) or Gleason ≥7 cancer with histological extraprostatic extension (csPCa-C),</p><p>Results</p><p>For csPCa-A, the Signal1 model (S_Max+S_Min) provided the best combination of signal-related variables, for both readers. The performance was improved by adding V_Max, ECE and/or dPSA, but not shape. All models performed better with DCE findings than without.</p><p>When moving from csPCa-A to csPCa-B and csPCa-C definitions, the added value of V_Max, dPSA and ECE increased as compared to signal-related variables, and the added value of DCE decreased.</p><p>For R1, the best models were Signal1+ECE+dPSA (AUC = 0,805 [95%CI:0,757–0,866]), Signal1+V_Max+dPSA (AUC = 0.823 [95%CI:0.760–0.893]) and Signal1+ECE+dPSA [AUC = 0.840 (95%CI:0.774–0.907)] for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0.844 [95%CI:0.806–0.877, p = 0.11], 0.841 [95%CI:0.799–0.876, p = 0.52]) and 0.849 [95%CI:0.811–0.884, p = 0.49], respectively.</p><p>For R2, the best models were Signal1+V_Max+dPSA (AUC = 0,790 [95%CI:0,731–0,857]), Signal1+V_Max (AUC = 0.813 [95%CI:0.746–0.882]) and Signal1+ECE+V_Max (AUC = 0.843 [95%CI: 0.781–0.907]) for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0. 829 [95%CI:0.791–0.868, p = 0.13], 0.790 [95%CI:0.742–0.841, p = 0.12]) and 0.808 [95%CI:0.764–0.845, p = 0.006]), respectively.</p><p>Conclusion</p><p>Combination of simple variables can match the Likert score’s results. The optimal combination depends on the definition of csPCa.</p></div

Assessed individual variables.

<p>Assessed individual variables.</p

Performance of models based on combinations of signal abnormalities in characterizing csPCa-A in PZ.

<p>Performance of models based on combinations of signal abnormalities in characterizing csPCa-A in PZ.</p

Axial multiparametric MR images acquired on scanner B at 3T, in a 58 year-old patient with a PSA density of 0.28 ng/mL/mL.

<p>A) T2-weighted image. B) Apparent diffusion coefficient map. C) Dynamic contrast-enhanced image. One suspicious lesion was described by both readers in the right peripheral zone (A-C, arrow). The lesion was noted as nodular without mass effect by both readers. S_T2, S_DW and S_DCE were respectively marked, marked and moderate for both readers. V_Max was 2.0 cc and 2.1 cc for readers 1 and 2 respectively. The ECE and Likert scores were respectively 2/5 and 5/5 for both readers. Analysis of the prostatectomy specimen showed a matching Gleason 9 (4+5) cancer with a histological volume of 1.6 cc.</p

Performances of multivariable models in characterizing csPCa-B in PZ.

<p>Performances of multivariable models in characterizing csPCa-B in PZ.</p

Evolution of diagnostic performances of models and Likert scores in PZ as a function of csPCa definition.

<p>Evolution of diagnostic performances of models and Likert scores in PZ as a function of csPCa definition.</p

Performances of multivariable models in characterizing csPCa-A in PZ.

<p>Performances of multivariable models in characterizing csPCa-A in PZ.</p