Disentangling the Association of Hydroxychloroquine Treatment with Mortality in Covid-19 Hospitalized Patients through Hierarchical Clustering.

The efficacy of hydroxychloroquine (HCQ) in treating SARS-CoV-2 infection is harshly debated, with observational and experimental studies reporting contrasting results. To clarify the role of HCQ in Covid-19 patients, we carried out a retrospective observational study of 4,396 unselected patients hospitalized for Covid-19 in Italy (February-May 2020). Patients' characteristics were collected at entry, including age, sex, obesity, smoking status, blood parameters, history of diabetes, cancer, cardiovascular and chronic pulmonary diseases, and medications in use. These were used to identify subtypes of patients with similar characteristics through hierarchical clustering based on Gower distance. Using multivariable Cox regressions, these clusters were then tested for association with mortality and modification of effect by treatment with HCQ. We identified two clusters, one of 3,913 younger patients with lower circulating inflammation levels and better renal function, and one of 483 generally older and more comorbid subjects, more prevalently men and smokers. The latter group was at increased death risk adjusted by HCQ (HR[CI95%] = 3.80[3.08-4.67]), while HCQ showed an independent inverse association (0.51[0.43-0.61]), as well as a significant influence of cluster∗HCQ interaction (p < 0.001). This was driven by a differential association of HCQ with mortality between the high (0.89[0.65-1.22]) and the low risk cluster (0.46[0.39-0.54]). These effects survived adjustments for additional medications in use and were concordant with associations with disease severity and outcome. These findings suggest a particularly beneficial effect of HCQ within low risk Covid-19 patients and may contribute to clarifying the current controversy on HCQ efficacy in Covid-19 treatment.

Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study.

BACKGROUND AND AIMS: There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death. METHODS AND RESULTS: Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a geographical gradient, Northern Italian regions featuring more than twofold higher death rates as compared to Central/Southern areas (15.6% vs 6.4%, respectively). Machine learning analysis revealed that the most important features in death classification were impaired renal function, elevated C reactive protein and advanced age. These findings were confirmed by multivariable Cox survival analysis (hazard ratio (HR): 8.2; 95% confidence interval (CI) 4.6-14.7 for age ≥85 vs 18-44 y); HR = 4.7; 2.9-7.7 for estimated glomerular filtration rate levels <15 vs ≥ 90 mL/min/1.73 m2; HR = 2.3; 1.5-3.6 for C-reactive protein levels ≥10 vs ≤ 3 mg/L). No relation was found with obesity, tobacco use, cardiovascular disease and related-comorbidities. The associations between these variables and mortality were substantially homogenous across all sub-groups analyses. CONCLUSIONS: Impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death in a large cohort of unselected patients with COVID-19, admitted to 30 different clinical centres all over Italy.

Ultrasound-guided transversus abdominis plane block (US-TAPb) for robot-assisted radical prostatectomy: a novel '4-point' technique-results of a prospective, randomized study.

Several works stress the importance of ultrasound-guided transversus abdominis plane block (TAPb) for post-operative analgesia and its versatility in all types of abdominal surgery, thanks to laparotomy and laparoscopy. The aim of this study was to evaluate the impact of TAPb on intra- and post-operative analgesia in the first 24 h after robot-assisted radical prostatectomy (RARP). TAPb is a new local anesthetic technique which provides analgesia after abdominal surgery. It involves injection of local anesthetic into the plane between the transversus abdominis and the internal oblique muscles. TAPb can be performed according to a landmark technique, either through the lumbar triangle or with ultrasound guidance. We evaluated the intra- and post-operative analgesic efficacy of TAPb in 100 ASA I-III patients undergoing RARP under general anesthesia without (group A, 50 patients) or with US-TAPb (group B, 50 patients), in the first 24 post-operative hours. After induction of general anesthesia, US-TAPb was performed in 50 selected patients. All patients received post-operative analgesia (Paracetamol 1 g) three times a day. Tramadol and Ketoprofen were used as rescue drugs if the Numerical Rating Scale test was > 3. No complications were recorded during block performance. A significant reduction was seen in the need to administer intraoperative opioids, and in the occurrence of post-operative pain or post-operative drug consumption in patients receiving US-TAPb. Seven patients, all in group A, received 100 mg of Tramadol. In group B, only one patient received 100 mg Tramadol (first and second post-operative days) due to surgical complications. In conclusion, US-TAPb provided highly effective intra- and post-operative analgesia in the first 24 h after RARP. A further prospective study is necessary to assess the best protocol for all patients.

Correction to: Ultrasound-guided transversus abdominis plane block (US-TAPb) for robot-assisted radical prostatectomy: a novel '4-point' technique-results of a prospective, randomized study.

The author would like to correct the errors in the publication of the original article due to multiple small errors in scientific content of article due to author oversight. Errors do not change or invalidate conclusion of article.