Can pelvic diameter measurement have an effect on surgical outcomes in radical cystectomy?

OBJECTIVE

To determine the effectiveness of pelvis diameters in determining postoperative outcomes in men who underwent open radical cystectomy + urinary diversion, it is aimed to predict the factors that may affect the operative difficulty and possible surgical outcomes before the operation.

METHODS

A total of 79 radical cystectomy patients operated in our institution with preoperative computed tomography (CT) were included the study. Pelvic dimensions; symphysis angle (SA), upper conjugate, lower conjugate, pelvic depth, apical depth (AD), interspinous distance (ISD), bone femoral width and soft tissue width were measured by preoperative CT. ISD index were defined as ISD/AD. Postoperative outcomes and indicators of operative difficulty were recorded. Regression analyses were used to predict perioperative and postoperative outcomes.

RESULTS

Total of 96 complications were observed in 52 of the 79 patients in ninety days (65,8%) with a mean age of 68.25 years. There were significant correlations between SA and body mass index (BMI) with operative time (p = 0.006, p < 0.001; respectively). For estimated blood loss, there were significant correlations between preoperative hematocrit (p = 0,031). Analysis of multivariate logistic regression revealed that higher Charlson comorbidity index (CCI) and BMI were found to be significant predictors for major complications while CCI, pathological T stage and ISD index are prominent predictors for surgical margin positivity.

CONCLUSIONS

Pelvic dimensions are not significant with minor or major complications. However, operative time may be associated with SA. Also, narrow and deep pelvis may increase the risk of positive surgical margins.

Image-guided percutaneous cryoablation of T1b renal cell carcinomas in patients with comorbidities

PURPOSE

To investigate the influence of comorbidities and tumor characteristics on outcomes following percutaneous cryoablation (PCA) of T1b renal cell carcinoma (RCC).

MATERIALS AND METHODS

Age-adjusted Charlson comorbidity index (ACCI); standardized system for quantitating renal tumor size, location, and depth (RENAL nephrometry score [RNS]); and local tumor control and survival were retrospectively investigated in 28 patients who underwent PCA for stage T1b RCC. Risk factors for elevated serum creatinine levels were also investigated.

RESULTS

Complete ablation was obtained in 27 of 28 patients. Two cases of metastasis were observed; one patient died 12 months after PCA. Overall survival at 5 years was 79.1%, with a mean follow-up of 42.0 ± 16.0 months. Local tumor control was not correlated with the ACCI and RNS. Worsening renal function 3 months after PCA was observed in ten patients, and it correlated with the presence of single kidneys (7/28 patients; p = 0.023). Significant worsening of renal function continued until 1 year after PCA (p = 0.013). Having a single kidney was a risk factor for worsened renal function after PCA (odds ratio, 8.00; 95% confidence interval 1.170-54.724).

CONCLUSION

PCA for T1b RCC confers positive local tumor control regardless of comorbidities and tumor characteristics.

Early laparoscopic cholecystectomy following percutaneous transhepatic gallbladder drainage is feasible in low-risk patients with acute cholecystitis

BACKGROUND

Laparoscopic cholecystectomy (Lap-C) is generally performed following percutaneous transhepatic gallbladder drainage (PTGBD) in patients with acute cholecystitis (AC). However, the timing of Lap-C and risk factors for postoperative complications following PTGBD are still unclear.

METHODS

We analyzed 331 patients with AC who underwent Lap-C following PTGBD. Univariate and multivariate logistic regression analyses were used for identifying risk factors associated with poor surgical outcomes, including postoperative complications in the total group and the early Lap-C subgroup (n = 152). Based on the Tokyo guideline 2013 (TG 13), all patients were divided into two groups according to the period (2009-2013, pre-TG 13 group; 2014-2020, post-TG 13 group), and each analysis was performed in those subgroups.

RESULTS

We found that early Lap-C (≤ 42 days after PTGBD) was associated with postoperative complications (OR 2.04, P = .022). Importantly, subgroup analyses revealed that Charlson comorbidity index (CCI) (OR 6.15, P < .001) and cholecystitis severity grade (OR 2.93, P = .014) were independent risk factors of postoperative complications in the early Lap-C group. Among the early Lap-C group, high CCI was also an independent risk factor for surgical complications in both pre-TG 13 (OR 14.87, P = .003) and post-TG 13 (OR 3.23, P = .046) groups. Interestingly, we found that the incidence of postoperative complications in the low-risk early Lap-C group was not different from the delayed group, even in the cases of very early surgery (≤ 1 week following PTGBD).

CONCLUSIONS

These findings suggest that early Lap-C is feasible following PTGBD, especially in low-risk patients, although future prospective large-scale studies are needed.

Adaptation of the Charlson Comorbidity Index for Register-Based Research in Sweden

Purpose

Comorbidity indices are often used to measure comorbidities in register-based research. We aimed to adapt the Charlson comorbidity index (CCI) to a Swedish setting.

Methods

Four versions of the CCI were compared and evaluated by disease-specific experts.

Results

We created a cohesive coding system for CCI to 1) harmonize the content between different international classification of disease codes (ICD-7,8,9,10), 2) delete incorrect codes, 3) enhance the distinction between mild, moderate or severe disease (and between diabetes with and without end-organ damage), 4) minimize duplication of codes, and 5) briefly explain the meaning of individual codes in writing.

Conclusion

This work may provide an integrated and efficient coding algorithm for CCI to be used in medical register-based research in Sweden.

Prognosis of hospitalized patients with community-acquired pneumonia

INTRODUCTION

The long-term prognosis of patients with community-acquired pneumonia (CAP) has attracted increasing interest in recent years. The objective of the present study is to investigate the short and long-term outcomes in hospitalized patients with CAP and to identify the predictive factors associated with mortality.

PATIENTS AND METHODS

The study was designed as a retrospective, multicenter, observational study. Hospitalized patients with CAP, as recorded in the pneumonia database of the Turkish Thoracic Society between 2011 and 2013, were included. Short-term mortality was defined as 30-day mortality and long-term mortality was assessed from those who survived 30 days. Predictive factors for short- and long-term mortality were analyzed.

RESULTS

The study included 785 patients, 68% of whom were male and the mean age was 67±16 (18-92). The median duration of follow-up was 61.2±11.8 (37-90) months. Thirty-day mortality was 9.2% and the median survival of patients surviving 30 days was 62.8±4.4 months. Multivariate analysis revealed that advanced age, the absence of fever, a higher Charlson comorbidity score, higher blood urea nitrogen (BUN)/albumin ratios and lower alanine aminotransferase (ALT) levels were all predictors of long-term mortality.

CONCLUSION

Long-term mortality following hospitalization for CAP is high. Charlson score and lack of fever are potential indicators for decreased long-term survival. As novel parameters, baseline BUN/albumin ratios and ALT levels are significantly associated with late mortality. Further interventions and closer monitoring are necessary for such subgroups of patients.

Adapting the Elixhauser comorbidity index for cancer patients

BACKGROUND

This study was designed to adapt the Elixhauser comorbidity index for 4 cancer-specific populations (breast, prostate, lung, and colorectal) and compare 3 versions of the Elixhauser comorbidity score (individual comorbidities, summary comorbidity score, and cancer-specific summary comorbidity score) with 3 versions of the Charlson comorbidity score for predicting 2-year survival with 4 types of cancer.

METHODS

This cohort study used Texas Cancer Registry-linked Medicare data from 2005 to 2011 for older patients diagnosed with breast (n = 19,082), prostate (n = 23,044), lung (n = 26,047), or colorectal cancer (n = 16,693). For each cancer cohort, the data were split into training and validation cohorts. In the training cohort, competing risk regression was used to model the association of Elixhauser comorbidities with 2-year noncancer mortality, and cancer-specific weights were derived for each comorbidity. In the validation cohort, competing risk regression was used to compare 3 versions of the Elixhauser comorbidity score with 3 versions of the Charlson comorbidity score. Model performance was evaluated with c statistics.

RESULTS

The 2-year noncancer mortality rates were 14.5% (lung cancer), 11.5% (colorectal cancer), 5.7% (breast cancer), and 4.1% (prostate cancer). Cancer-specific Elixhauser comorbidity scores (c = 0.773 for breast cancer, c = 0.772 for prostate cancer, c = 0.579 for lung cancer, and c = 0.680 for colorectal cancer) performed slightly better than cancer-specific Charlson comorbidity scores (ie, the National Cancer Institute combined index; c = 0.762 for breast cancer, c = 0.767 for prostate cancer, c = 0.578 for lung cancer, and c = 0.674 for colorectal cancer). Individual Elixhauser comorbidities performed best (c = 0.779 for breast cancer, c = 0.783 for prostate cancer, c = 0.587 for lung cancer, and c = 0.687 for colorectal cancer).

CONCLUSIONS

The cancer-specific Elixhauser comorbidity score performed as well as or slightly better than the cancer-specific Charlson comorbidity score in predicting 2-year survival. If the sample size permits, using individual Elixhauser comorbidities may be the best way to control for confounding in cancer outcomes research. Cancer 2018;124:2018-25. © 2018 American Cancer Society.

Predicting Disability after Ischemic Stroke Based on Comorbidity Index and Stroke Severity-From the Virtual International Stroke Trials Archive-Acute Collaboration

Background and aim

The availability and access of hospital administrative data [coding for Charlson comorbidity index (CCI)] in large data form has resulted in a surge of interest in using this information to predict mortality from stroke. The aims of this study were to determine the minimum clinical data set to be included in models for predicting disability after ischemic stroke adjusting for CCI and clinical variables and to evaluate the impact of CCI on prediction of outcome.

Method

We leverage anonymized clinical trial data in the Virtual International Stroke Trials Archive. This repository contains prospective data on stroke severity and outcome. The inclusion criteria were patients with available stroke severity score such as National Institutes of Health Stroke Scale (NIHSS), imaging data, and outcome disability score such as 90-day Rankin Scale. We calculate CCI based on comorbidity data in this data set. For logistic regression, we used these calibration statistics: Nagelkerke generalised R² and Brier score; and for discrimination we used: area under the receiver operating characteristics curve (AUC) and integrated discrimination improvement (IDI). The IDI was used to evaluate improvement in disability prediction above baseline model containing age, sex, and CCI.

Results

The clinical data among 5,206 patients (55% males) were as follows: mean age 69 ± 13 years, CCI 4.2 ± 0.8, and median NIHSS of 12 (IQR 8, 17) on admission and 9 (IQR 5, 15) at 24 h. In Model 2, adding admission NIHSS to the baseline model improved AUC from 0.67 (95% CI 0.65–0.68) to 0.79 (95% CI 0.78–0.81). In Model 3, adding 24-h NIHSS to the baseline model resulted in substantial improvement in AUC to 0.90 (95% CI 0.89–0.91) and increased IDI by 0.23 (95% CI 0.22–0.24). Adding the variable recombinant tissue plasminogen activator did not result in a further change in AUC or IDI to this regression model. In Model 3, the variable NIHSS at 24 h explains 87.3% of the variance of Model 3, follow by age (8.5%), comorbidity (3.7%), and male sex (0.5%).

Conclusion

Our results suggest that prediction of disability after ischemic stroke should at least include 24-h NIHSS and age. The variable CCI is less important for prediction of disability in this data set.