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

OBJECTIVES: 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.

Adverse drug reactions associated with immune checkpoint inhibitors: An exploratory nested case-control study in a historical cohort.

INTRODUCTION: Data on adverse drug reactions (ADRs) of immune checkpoint inhibitors (ICIs) used in oncology are mainly derived from clinical trials or cancer-specific reviews. We aim to analyze ADRs that occurred in patients treated with ICIs in real life. MATERIALS AND METHODS: We conducted an observational study on a historical cohort of the University Hospitals of Lyon. All patients who initiated an ICI treatment for any cancer in 2017 were included. Patients were followed from the first infusion until 90 days after the last one, death, date of last news or end of the study period (28 February 2019), whichever came first. Two pharmacovigilance specialists assessed the accountability and the severity of each ADR using Naranjo algorithm and common terminology criteria for adverse events (CTCAE) classification, respectively. RESULTS: 248 patients were included. They were treated with anti-PD-(L)-1, mainly nivolumab (70.6%) and pembrolizumab (25.8%). Lung cancer (62.1%) and melanoma (20.2%) were the most represented cancers. 139 ADRs occurred in 93 patients (37.5%), on average at the 6th cure (±6.8). ADRs mainly concerned skin (29.5%), endocrine (19.4%) and digestive (10.8%) systems. 17.3% of ADRs were grades III-V and two patients died because of ADRs. By comparing patients with (N=93) or without (N=155) ADRs, all characteristics appeared similar except for age, number of infusions received and death status. The spontaneous notification rate found in this study was 5.8% for all grade ADRs (N = 8) but raised to 23.8% when only grades higher than III were considered (N = 5). DISCUSSION/CONCLUSION: Our results are consistent with literature data in frequency and type of serious ADRs. We found a lower frequency of ADRs of any grade, which could be explained by a fairer causality assessment in our study than in clinical trials.

Replacing liquid with solid dosage forms in pediatric practice: Feasibility and economic impact from a hospital-based study.

AIM OF THE STUDY: To identify the 10 drugs most frequently administered to children in liquid dosage forms which are eligible for replacement with suitable authorized solid dosage forms and to assess the expected economic impact of this substitution. METHODS: The health record data from 312,152 oral drug administrations were analyzed. Ten drugs were selected according to their frequency of administration in liquid dosage forms, the availability of solid form alternatives, and the suitability of these alternatives for the children receiving the corresponding liquid forms. Potential hospital cost savings of the suggested substitutions were calculated. RESULTS: The 10 drugs identified as most frequently administered and for which suitable solid forms were available were: paracetamol, cyamemazine, valproic acid, clonazepam, furosemide, prazepam, hydroxyzine, alfacalcidol, amitriptyline, and levetiracetam. Thirty-four point six of the administrations of these drugs in liquid dosage forms could be delivered using suitable solid dosage forms without additional cost. CONCLUSION: Opportunities exist for substituting liquid dosage forms with market-available solid dosage forms suitable in size and dosage for the pediatric population.

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.

Impact of the First Wave of the COVID-19 Pandemic on the Lyon University Hospital Cancer Institute (IC-HCL).

This article presents the protective measures put in place at the "Institut de Cancérologie des Hospices de Lyon" (IC-HCL) during the first wave of the COVID-19 pandemic in France (spring 2020) and how they impacted IC-HCL clinical activity. Spring 2020 activities were compared to winter 2019-2020. Results showed a decrease of activity of 9% for treatment dispensations, 17% for multidisciplinary team meetings, 20% for head and neck and thoracic surgeries, and 58% for new patient enrolment in clinical trials. Characteristics of patients treated for solid cancer and hospitalized for COVID-19 during spring 2020 were collected in a retrospective study. Mortality was attributed to COVID-19 for half of the cases, 82% being patients above 70 and 73% being stage IV. This is in concordance with current findings concluding that the risk of developing severe or critical symptoms of COVID-19 is correlated with factors co-occurring in cancer patients and not to the cancer condition per se. While a number of routines and treatment regimens were changed, there was no major decline in numbers of treatments conducted at the IC-HCL during the first wave of the COVID-19 pandemic that hit France between March and May 2020, except for clinical trials and some surgery activities.

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

PURPOSE: To assess the diagnostic weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers (csPCa). MATERIALS AND METHODS: 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). RESULTS: 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. 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. 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. 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. CONCLUSION: Combination of simple variables can match the Likert score's results. The optimal combination depends on the definition of csPCa.