Combined Charlson comorbidity/C-Reactive Protein Index Is a Novel Predictor in Renal Cell Carcinoma: Analysis of the International Marker Consortium for Renal Cancer (INMARC) Registry.

OBJECTIVE: To evaluate predictive ability of a novel combined index, Charlson comorbidity index and C-reactive protein (CCI-CRP), for outcomes in renal cell carcinoma (RCC), and compare predictive outcomes with of CCI-CRP to its separate components and to the UCLA integrated staging system (UISS). PATIENTS AND METHODS: We retrospectively analyzed INMARC registry of RCC patients. Receiver Operator Characteristics (ROC) analysis was fitted to identify threshold defining low-CRP (LCRP) and high-CRP (HCRP). Patients were stratified according to CCI [low-CCI ≤ 3 (LCCI); intermediate-CCI 4-6 (ICCI); high-CCI > 6 (HCCI)] and CRP level. Kaplan-Meier analysis (KMA) was conducted for overall (OS) and cancer-specific survival (CSS). Based on survival analysis distribution we proposed a new stratification: CCI-CRP. Model performance was assessed with ROC/area under the curve (AUC) analysis and compared to CCI and CRP alone, and UISS. RESULTS: We analyzed 2,890 patients (median follow-up 30 months). ROC identified maximum product sensitivity and specificity for CRP at 3.5 mg/L. KMA revealed 5-year OS of 95.6% for LCRP/LCCI, 83% LCRP/ICCI, 73.3% LCRP/HCCI, 62.6% HCRP/LCCI, 51.6% HCRP/ICCI and 40.5% HCRP/HCCI (P < .001). From this distribution, new CCI-CRP is proposed: low CCI-CRP (LCRP/LCCI and LCRP/ICCI), intermediate CCI-CRP (LCRP/HCCI and HCRP/LCCI), and high CCI-CRP (HCRP/ICCI and HCRP/HCCI). AUC for CCI-CRP showed improved performance for predicting OS/CSS vs. CCI alone (0.73 vs. 0.63/0.77 vs. 0.60), CRP alone (0.73 vs. 0.71/0.77 vs. 0.74) and UISS (0.73 vs 0.67/0.77 vs 0.73). CONCLUSIONS: CCI-CRP, exhibits increased prognostic performance for survival outcomes in RCC compared to CCI and CRP alone, and UISS. Further investigation is requisite.

The Effect of the Charlson Comorbidity Index On In-Hospital Complications, Hospital Length of Stay, Mortality, and Readmissions Among Patients Hospitalized for Acute Stroke.

Aim A notable number of people who develop stroke have comorbid medical conditions. The aim of this study is to evaluate the use of the Charlson Comorbidity Index (CCI) to predict in-hospital complications, mortality, length of stay, and readmission rates in stroke patients. Method It is a retrospective study that analyzed patients who were admitted for stroke in a six-month time duration. Stroke was classified into ischemic, hemorrhagic, or undetermined; hospital complications were classified into medical or neurological. Data regarding comorbidities, complications, length of stay, mortality, and readmissions were documented. Comorbidities were then classified by the CCI and split into four categories: zero, mild (1-2), moderate (3-4) and severe (5+). The data was analyzed using SPSS (IBM, Inc., Armonk, US). Results Four hundred and seventy-three adults aged above 18 were hospitalized for acute stroke. There was no correlation between the severity of the CCI score and mortality. Patients with ischemic stroke had a higher CCI correlated with readmission rate (p=0.026) and hospital complications (p=0.054). The two groups with the highest intensive care unit admission rate were mild, followed by the severe group (p=0.001). Our study also revealed that the patients with severe CCI scores had an increased readmission rate (p=0.001). Conclusion There is a correlation between a high CCI score and readmission rate, as well as CCI score with hospital complications in ischemic stroke. Further prospective studies of a longer duration can be undertaken to find further associations with the potential for this score to be used as a predictor in patients hospitalized for stroke.

Nomogram based on immune-inflammatory indicators and age-adjusted charlson comorbidity index score to predict prognosis of postoperative parotid gland carcinoma patients.

BACKGROUND: Parotid gland carcinoma (PGC) is a rare malignant tumor. The purpose of this study was to investigate the role of immune-inflammatory-nutrition indicators and age-adjusted Charlson comorbidity index score (ACCI) of PGC and develop the nomogram model for predicting prognosis. METHOD: All patients diagnosed with PGC in two tertiary hospitals, treated with surgical resection, from March 2012 to June 2018 were obtained. Potential prognostic factors were identified by univariate and multivariate Cox regression analyses. The nomogram models were established based on these identified independent prognostic factors. The performance of the developed prognostic model was estimated by related indexes and plots. RESULT: The study population consisted of 344 patients with PGC who underwent surgical resection, 285 patients without smoking (82.8%), and 225 patients (65.4%) with mucoepidermoid carcinoma, with a median age of 50.0 years. American Joint Committee on Cancer (AJCC) stage (p < 0.001), pathology (p = 0.019), tumor location (p < 0.001), extranodal extension (ENE) (p < 0.001), systemic immune-inflammation index (SII) (p = 0.004), prognostic nutrition index (PNI) (p = 0.003), ACCI (p < 0.001), and Glasgow prognostic Score (GPS) (p = 0.001) were independent indicators for disease free survival (DFS). Additionally, the independent prognostic factors for overall survival (OS) including AJCC stage (p = 0.015), pathology (p = 0.004), tumor location (p < 0.001), perineural invasion (p = 0.009), ENE (p < 0.001), systemic immune-inflammation index (SII) (p = 0.001), PNI (p = 0.001), ACCI (p = 0.003), and GPS (p = 0.033). The nomogram models for predicting DFS and OS in PGC patients were generated based on these independent risk factors. All nomogram models show good discriminative capability with area under curves (AUCs) over 0.8 (DFS 0.802, and OS 0.825, respectively). Decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification index (NRI) show good clinical net benefit of the two nomograms in both training and validation cohorts. Kaplan-Meier survival analyses showed superior discrimination of DFS and OS in the new risk stratification system compared with the AJCC stage system. Finally, postoperative patients with PGC who underwent adjuvant radiotherapy had a better prognosis in the high-, and medium-risk subgroups (p < 0.05), but not for the low-risk subgroup. CONCLUSION: The immune-inflammatory-nutrition indicators and ACCI played an important role in both DFS and OS of PGC patients. Adjuvant radiotherapy had no benefit in the low-risk subgroup for PGC patients who underwent surgical resection. The newly established nomogram models perform well and can provide an individualized prognostic reference, which may be helpful for patients and surgeons in proper follow-up strategies.

Prognostic value and association of the age-adjusted Charlson Comorbidity Index with sarcopenia within patients with gastric cancer after radical resection.

PURPOSE: The association between the age-adjusted Charlson Comorbidity Index (ACCI) and sarcopenia in patients with gastric cancer (GC) remains ambiguous. This study aimed to investigate the association between the ACCI and sarcopenia and the prognostic value in patients with GC after radical resection. In addition, this study aimed to develop a novel prognostic scoring system based on these factors. METHODS: Univariate and multivariate Cox regression analyses were used to determine prognostic factors in patients undergoing radical GC resection. Based on the ACCI and sarcopenia, a new prognostic score (age-adjusted Charlson Comorbidity Index and Sarcopenia [ACCIS]) was established, and its prognostic value was assessed. RESULTS: This study included 1068 patients with GC. Multivariate analysis revealed that the ACCI and sarcopenia were independent risk factors during the prognosis of GC (P = 0.001 and P < 0.001, respectively). A higher ACCI score independently predicted sarcopenia (P = 0.014). A high ACCIS score was associated with a greater American Society of Anesthesiologists score, higher pathologic TNM (pTNM) stage, and larger tumor size (all P < 0.05). Multivariate analysis demonstrated that the ACCIS independently predicted the prognosis for patients with GC (P < 0.001). By incorporating the ACCIS score into a prognostic model with sex, pTNM stage, tumor size, and tumor differentiation, we constructed a nomogram to predict the prognosis accurately (concordance index of 0.741). CONCLUSION: The ACCI score and sarcopenia are significantly correlated in patients with GC. The integration of the ACCI score and sarcopenia markedly enhances the accuracy of prognostic predictions in patients with GC.

Exploring Risk Factors for Predicting 30-Day Postoperative Morbidity in Musculoskeletal Tumor Surgery.

Background: This study investigates the risk factors associated with postoperative complications in musculoskeletal tumor surgeries and evaluates the impact of benchmarking in enhancing surgical outcomes. Methods: Conducted at a tertiary referral center, this retrospective analysis included 196 patients who underwent surgeries for various musculoskeletal tumors, ranging from soft tissue to bone sarcomas. Patient and tumor characteristics, along with surgical interventions and outcomes, were comprehensively assessed using the Charlson Comorbidity Index and the Clavien-Dindo classification. Results: Key findings indicate that surgical reconstruction, ASA 3 status, bone tumor presence, and the need for multiple erythrocyte transfusions significantly increase postoperative morbidity. Notably, no significant correlation was found between the Charlson Comorbidity Index scores and the occurrence or severity of complications, challenging the utility of this index in predicting short-term surgical outcomes. Conclusions: This study highlights the importance of tailored surgical approaches and emphasizes rigorous preoperative assessments to mitigate risks and enhance patient care. Despite its insights, limitations include its retrospective nature and single-center scope, suggesting a need for broader, multicenter studies to generalize findings. Overall, our results underscore the necessity of integrating clinical assessments with benchmarking data to optimize outcomes in the complex field of musculoskeletal tumor surgery.

Impact of Charlson Comorbidity Index on in-hospital mortality of patients with hyperglycemic crises: A propensity score matching analysis.

AIM: This study was designed to investigate the association between Charlson Comorbidity Index (CCI) and in-hospital mortality and other clinical outcomes among patients with hyperglycemic crises. METHOD: This retrospective cohort study was conducted using data from electric medical records. A total of 1668 diabetic patients with hyperglycemic crises from six tertiary hospitals met the inclusion criteria. CCI < 4 was defined as low CCI and CCI ≥ 4 was defined as high CCI. Propensity score matching (PSM) with the 1:1 nearest neighbour matching method and the caliper value of 0.02 was used to match the baseline characteristics of patients with high CCI and low CCI to reduce the confounding bias. In-hospital mortality, ICU admission, hypoglycemia, hypokalemia, acute kidney injury, length of stay (LOS), and hospitalisation expense between low CCI and high CCI were compared and assessed. Univariate and multivariate regression were applied to estimate the impact of CCI on in-hospital and other clinical outcomes. OUTCOME: One hundred twenty-one hyperglycemic crisis (HC) patients died with a mortality rate of 7.3%. After PSM, compared with low CCI, patients with high CCI suffered higher in-hospital mortality, ICU admission, LOS, and hospitalisation expenses. After multivariate regression, age (aOR: 1.12, 95% confidence interval [CI]: 1.06-1.18, p < 0.001), CCI(aOR: 4.42, 95% CI: 1.56-12.53, p = 0.005), uninsured (aOR: 22.32, 95% CI: 4.26-116.94, p < 0.001), shock (aOR: 10.57, 95% CI: 1.41-79.09, p = 0.022), mechanical ventilation (aOR: 75.29, 95% CI: 12.37-458.28, p < 0.001), and hypertension (aOR: 4.34, 95% CI: 1.37-13.82, p = 0.013) were independent risk factors of in-hospital mortality of HC patients. Besides, high CCI was an independent risk factor for higher ICU Admission (aOR: 5.91, 95% CI: 2.31-15.08, p < 0.001), hypoglycemia (aOR: 2.19, 95% CI:1.01-4.08, p = 0.049), longer LOS (aOR: 1.23, 95% CI: 1.19-2.27, p = 0.021), and higher hospitalisation expense (aOR: 2089.97, 95% CI: 193.33-3988.61, p = 0.031) of HC patients. CONCLUSION: CCI is associated with in-hospital mortality, ICU admission, hypoglycemia, LOS, and hospitalisation expense of HC patients. CCI could be an ideal indicator to identify, monitor, and manage chronic comorbidities among HC patients.

Comorbidity burden and outcomes after mechanical thrombectomy for large vessel occlusion: A retrospective analysis.

OBJECTIVES: Patients with acute ischemic stroke (AIS) often have an accumulation of pre-existing comorbidities, but its clinical impact on outcomes after mechanical thrombectomy (MT) remains unknown. Therefore, we examined whether comorbidity burden before AIS onset could predict clinical outcomes after MT. METHODS: In this retrospective cohort, we enrolled consecutive patients with community-onset AIS who underwent MT between April 2016 and December 2021. To evaluate each patient's comorbidity burden, we calculated Charlson comorbidity index (CCI), then classified the patients into the High CCI (≥ 3) and the Low CCI (< 3) groups. The primary outcome was a good neurological outcome at 90 days, defined as a modified Rankin scale 0-2 or no worse than the previous daily conditions. All-cause mortality at 90 days and hemorrhagic complications after MT were also compared between the two groups. We estimated the odds ratios and their confidence intervals using a multivariable logistic regression model. RESULTS: A total of 388 patients were enrolled, of whom 86 (22.2%) were classified into the High CCI group. Patients in the High CCI group were less likely to achieve a good neurological outcome (adjusted odds ratio of 0.26 [95% confidence interval, 0.12-0.58]). Moreover, symptomatic intracranial hemorrhage was more common in the High CCI (14.0% vs. 4.6%; adjusted odds ratio, 4.10 [95% confidence interval, 1.62-10.3]). CONCLUSIONS: Comorbidity burden assessed by CCI was associated with clinical outcomes after MT. CCI has the potential to become a simple and valuable tool for predicting neurological prognosis among patients with AIS and MT.

Age-adjusted Charlson comorbidity index is associated with the risk of osteoporosis in older fall-prone men: a retrospective cohort study.

BACKGROUND: There is growing evidence linking the age-adjusted Charlson comorbidity index (aCCI), an assessment tool for multimorbidity, to fragility fracture and fracture-related postoperative complications. However, the role of multimorbidity in osteoporosis has not yet been thoroughly evaluated. We aimed to investigate the association between aCCI and the risk of osteoporosis in older adults at moderate to high risk of falling. METHODS: A total of 947 men were included from January 2015 to August 2022 in a hospital in Beijing, China. The aCCI was calculated by counting age and each comorbidity according to their weighted scores, and the participants were stratified into two groups by aCCI: low (aCCI < 5), and high (aCCI ≥5). The Kaplan Meier method was used to assess the cumulative incidence of osteoporosis by different levels of aCCI. The Cox proportional hazards regression model was used to estimate the association of aCCI with the risk of osteoporosis. Receiver operating characteristic (ROC) curve was adapted to assess the performance for aCCI in osteoporosis screening. RESULTS: At baseline, the mean age of all patients was 75.7 years, the mean BMI was 24.8 kg/m2, and 531 (56.1%) patients had high aCCI while 416 (43.9%) were having low aCCI. During a median follow-up of 6.6 years, 296 participants developed osteoporosis. Kaplan-Meier survival curves showed that participants with high aCCI had significantly higher cumulative incidence of osteoporosis compared with those had low aCCI (log-rank test: P < 0.001). When aCCI was examined as a continuous variable, the multivariable-adjusted model showed that the osteoporosis risk increased by 12.1% (HR = 1.121, 95% CI 1.041-1.206, P = 0.002) as aCCI increased by one unit. When aCCI was changed to a categorical variable, the multivariable-adjusted hazard ratios associated with different levels of aCCI [low (reference group) and high] were 1.00 and 1.557 (95% CI 1.223-1.983) for osteoporosis (P <  0.001), respectively. The aCCI (cutoff ≥5) revealed an area under ROC curve (AUC) of 0.566 (95%CI 0.527-0.605, P = 0.001) in identifying osteoporosis in older fall-prone men, with sensitivity of 64.9% and specificity of 47.9%. CONCLUSIONS: The current study indicated an association of higher aCCI with an increased risk of osteoporosis among older fall-prone men, supporting the possibility of aCCI as a marker of long-term skeletal-related adverse clinical outcomes.

Charlson and Elixhauser Comorbidity Indices for Prediction of Mortality and Hospital Readmission in Patients With Acute Pulmonary Embolism.

Several risk stratification systems aid clinicians in classifying pulmonary embolism (PE) severity and prognosis. We compared 2 clinical PE scoring systems, the PESI and sPESI scores, with 2 comorbidity indices, the Charlson Comorbidity Index (CCI) and the val Walraven Elixhauser Comorbidity Index (ECI), to determine the utility of each in predicting mortality and hospital readmission. Information was collected from 436 patients presenting with PE via retrospective chart review. The PESI, sPESI, CCI, and ECI scores were calculated for each patient. Multivariate analysis was used to determine each system's ability to predict in-hospital mortality, 90-day mortality, overall mortality, and all-cause hospital readmission. The impact of various demographic and clinical characteristics of each patient on these outcomes was also assessed. The PESI score was found to be an independent predictor of in-hospital mortality and 90-day mortality. The PESI score and the CCI were able to independently predict overall mortality. None of the 4 risk scores independently predicted hospital readmission. Other factors including hypoalbuminemia, serum BNP, coagulopathy, anemia, and diabetes were associated with increased mortality and readmission at various endpoints. The PESI score was the best tool for predicting mortality at any endpoint. The CCI may have utility in predicting long-term outcomes. Further work is needed to better determine the roles of the CCI and ECI in predicting patient outcomes in PE. The potential prognostic implications of low serum albumin and anemia at the time of PE also warrant further investigation.

Characteristics of Comorbid Physical Disease in Patients With Severe Mental Illness in South Korea: A Nationwide Population-Based Study (2014-2019).

OBJECTIVE: The purpose of this study was to identify the associations of chronic physical disease between patients with severe mental illness (SMI) and the general population of South Korea. METHODS: This study was conducted with National Health Insurance Corporation data from 2014 to 2019. A total of 848,058 people were diagnosed with SMI in this period, and the same number of controls were established by matching by sex and age. A descriptive analysis was conducted on the sociodemographic characteristics of patients with SMI. Conditional logistic regression analysis was performed to identify the associations between comorbid physical disease in patients with SMI and those of the general population. SAS Enterprise Guide 7.1 (SAS Inc, Cary, NC, USA) were used to perform all statistical tests. RESULTS: The analysis revealed significant differences in medical insurance, income level, and Charlson Comorbidity Index weighted by chronic physical disease, between patients with SMI and the general population. Conditional logistic regression analysis between the two groups also revealed significant differences in eight chronic physical diseases except hypertensive disease. CONCLUSION: This study confirmed the vulnerability of patients with SMI to chronic physical diseases and we were able to identify chronic physical disease that were highly related to patients with SMI.

Ventriculoperitoneal Shunt Placement Safety in Idiopathic Normal Pressure Hydrocephalus: Anticoagulated Versus Non-Anticoagulated Patients.

BACKGROUND: Many patients with idiopathic normal pressure hydrocephalus (iNPH) have medical comorbidities requiring anticoagulation that could negatively impact outcomes. This study evaluated the safety of ventriculoperitoneal shunt placement in iNPH patients on systemic anticoagulation versus those not on anticoagulation. METHODS: Patients >60 years of age with iNPH who underwent shunting between 2018 and 2022 were retrospectively reviewed. Baseline demographics, comorbidities (quantified by modified frailty index and Charlson comorbidity index), anticoagulant/antiplatelet agent use (other than aspirin), operative details, and complications were collected. Outcomes of interest were the occurrence of postoperative hemorrhage and overdrainage. RESULTS: A total of 234 patients were included in the study (mean age 75.22 ± 6.04 years; 66.7% male); 36 were on anticoagulation/antiplatelet therapy (excluding aspirin). This included 6 on Warfarin, 19 on direct Xa inhibitors, 10 on Clopidogrel, and 1 on both Clopidogrel and Warfarin. Notably, 70% of patients (164/234) used aspirin alone or combined with anticoagulation or clopidogrel. Baseline modified frailty index was similar between groups, but those on anticoagulant/antiplatelet therapy had a higher mean Charlson comorbidity index (2.67 ± 1.87 vs. 1.75 ± 1.84; P = 0.001). Patients on anticoagulants were more likely to experience tract hemorrhage (11.1 vs. 2.5%; P = 0.03), with no significant difference in the rates of intraventricular hemorrhage or overdrainage-related subdural fluid collection. CONCLUSIONS: Anticoagulant and antiplatelet agents are common in the iNPH population, and patients on these agents experienced higher rates of tract hemorrhage following ventriculoperitoneal shunt placement; however, overall hemorrhagic complication rates were similar.

Comparing Charlson Comorbidity Index Scores between Anesthesiologists, Patients, and Administrative Data: A Prospective Observational Study.

(1) Background: Patients' comorbidities play an immanent role in perioperative risk assessment. It is unknown how Charlson Comorbidity Indices (CCIs) from different sources compare. (2) Methods: In this prospective observational study, we compared the CCIs of patients derived from patients' self-reports and from physicians' assessments with hospital administrative data. (3) Results: The data of 1007 patients was analyzed. Agreement between the CCI from patients' self-report compared to administrative data was fair (kappa 0.24 [95%CI 0.2-0.28]). Agreement between physicians' assessment and the administrative data was also fair (kappa 0.28 [95%CI 0.25-0.31]). Physicians' assessment and patients' self-report had the best agreement (kappa 0.33 [95%CI 0.30-0.37]). The CCI calculated from the administrative data showed the best predictability for in-hospital mortality (AUROC 0.86 [95%CI 0.68-0.91]), followed by equally good prediction from physicians' assessment (AUROC 0.80 [95%CI 0.65-0.94]) and patients' self-report (AUROC 0.80 [95%CI 0.75-0.97]). (4) Conclusions: CCIs derived from patients' self-report, physicians' assessments, and administrative data perform equally well in predicting postoperative in-hospital mortality.

Evaluation of comorbidity scoring systems in patients undergoing knee arthroplasty.

BACKGROUND: Ageing populations are increasing the demand for knee arthroplasty. Concurrently, the prevalence of medical comorbidities are rising too. The Self-Administered Comorbidity Questionnaire was developed to provide a patient's assessment of their own comorbidities whereas the American Society of Anesthesiologists grades and the Charlson Comorbidity Index utilise clinical evaluation to objectively measure perioperative morbidity and mortality risk. The primary aim of this study was to compare Self-Administered Comorbidity Questionnaire scores with American Society of Anesthesiologists grades and Charlson Comorbidity Index scores. The secondary aim was to compare Self-Administered Comorbidity Questionnaire scores with knee outcome scores. METHODS: A single centre observational cohort study of patients with knee osteoarthritis undergoing elective knee arthroplasty. Preoperative evaluation included Self-Administered Comorbidity Questionnaire scores, American Society of Anesthesiologists grades, Charlson Comorbidity Index scores and validated patient-reported outcome measures specific to knee surgery. RESULTS: A total of 141 patients were included in this study. Self-Administered Comorbidity Questionnaire scores were directly correlated with American Society of Anesthesiologists grade (rho = 0.37, p < 0.001) and Charlson Comorbidity Index scores (rho = 0.19, p = 0.047). Individual American Society of Anesthesiologists grades had significantly different Self-Administered Comorbidity Questionnaire scores (p = 0.001). Self-Administered Comorbidity Questionnaire scores were specifically associated with hypertension, ischaemic heart disease, chronic obstructive pulmonary disease and the total number of comorbidities, but American Society of Anesthesiologists and Charlson Comorbidity Index scores were associated with more comorbidities. Overall, Self-Administered Comorbidity Questionnaire scores were inversely correlated with patient-reported outcome measure scores. CONCLUSION: Self-Administered Comorbidity Questionnaire scores are associated with increasing comorbidity in patients with symptomatic knee osteoarthritis; however, American Society of Anesthesiologists grades and Charlson Comorbidity Index scores had stronger and more abundant associations with comorbidities and patient-reported outcome measure scores. Self-Administered Comorbidity Questionnaires may complement but not replace current objective assessments of comorbidity when evaluating perioperative risk for knee arthroplasty.

A comparative analysis of Charlson's weighted index of comorbidity and perforated peptic ulcer scores in predicting postoperative mortality and high comprehensive complication index.

BACKGROUND: Limited data exists on Charlson's weighted index of comorbidity (WIC) predictability for postoperative outcomes following perforated peptic ulcer (PPU) surgery. This study assesses the utility of WIC and other predictive scores in forecasting both postoperative mortality and morbidity in PPU. MATERIALS & METHODS: Patients with PPUs operated between 2018 and 2021 in a Malaysian tertiary referral center were included. Clinical data were retrospectively analyzed for association with mortality and morbidity measured with the Comprehensive Complication Index (CCI). Predictability of WIC and other predictors were examined using area under receiver-operator characteristic (ROC) curve (AUC). RESULTS: Among 110 patients included, 18 died (16.4%) and 36 (32.7%) had significant morbidity postoperatively (High CCI, ≥26.2). Both mortality and high CCI were associated with age >65 years, female sex, comorbidities (diabetes mellitus, hypertension, and renal disease), and American Society of Anesthesiologist score >2. Most patients who died had renal dysfunction, metabolic acidosis, lactate >2 mmol/L upon presentation preoperatively. While surgery >24 h after presentation correlated with mortality and high CCI, the benefit of earlier surgery <6 h or <12 h was not demonstrated. WIC (AUC, 0.89; 95% CI, 0.81-0.99) showed similar predictability to Peptic Ulcer Perforation (PULP) (AUC, 0.97; 95% CI, 0.93-1.00) for mortality. PULP effectively predicted high CCI (AUC, 0.83; 95% CI, 0.73-0.93; p < 0.001). CONCLUSION: WIC is valuable in predicting mortality, highlighting the importance of comorbidity in risk assessment. PULP score was effective in predicting both mortality and high CCI. Early identification of patients with high perioperative risk will facilitate patients' triage for escalated care, leading to a better outcome.

Can The Systemic Immune-Inflammation Index (SII) and Charlson Comorbidity Index (CCI) be used to predict mortality in patients with necrotizing fasciitis?

PURPOSE: This study aimed to determine the impact of mortality and morbidity indices on the diagnosis and prognosis of patients suffering from necrotizing fasciitis. METHODS: A retrospective analysis was performed on 41 patients (26 females, 15 males) with necrotizing fasciitis (NF). The SII (Systemic Immune-Inflammation Index) was computed using the formula SII = (P × N)/L, where P, N, and L measure the counts of peripheral platelets, neutrophils, and lymphocytes, respectively. This study evaluated the clinicopathological characteristics and follow-up information to assess the comparative effectiveness of SII, CCI (Charlson Comorbidity Index), and LRINEC (Laboratory Risk Indicator for Necrotizing Fasciitis) scores as mortality and morbidity indices for patients with NF. RESULTS: The optimal cut-off for SII was determined to be 455. The SII value in the group with mortality was significantly higher compared to the group without mortality (p < 0.05). The CCI value in the group with mortality was significantly higher than the group without mortality (p < 0.05). The SII and CCI values were found to be effective in distinguishing between patients who suffered mortality and those who did not. CONCLUSION: SII is a powerful tool for predicting mortality in patients with necrotizing fasciitis (NF). The SII index provides a novel, easily accessible, and inexpensive indicator for monitoring the progress and predicting the survival of patients with NF.

Categorization of Patients with Pulmonary Embolism by Charlson Comorbidity Index.

BACKGROUND: Short-term outcomes of pulmonary embolism are closely related to right ventricular dysfunction and patient's hemodynamic status, but also to individual comorbidity profile. However, the impact of patients' comorbidities on survival during pulmonary embolism might be underrated. Although the Charlson Comorbidity Index (CCI) is the most extensively studied comorbidity index for detecting comorbidity burden, studies analyzing the impact of CCI on pulmonary embolism patients' survival are limited. METHODS: We used the German nationwide inpatient sample to analyze all hospitalized patients with pulmonary embolism in Germany 2005-2020 and calculated CCI for each patient, compared the CCI classes (very low: CCI = 0 points, mild: CCI = 1-2 points, moderate: CCI = 3-4, high severity: CCI >4 points) and impact of CCI class on outcomes. RESULTS: Overall, 1,373,145 hospitalizations of patients with acute pulmonary embolism (53.0% females, 55.9% aged ≥70 years) were recorded in Germany between 2005 and 2020; the CCI class stratified them. Among these, 100,156 (7.3%) were categorized as very low; 221,545 (16.1%) as mild; 394,965 (28.8%) as moderate; and 656,479 (47.8%) as patients with a high comorbidity burden according to CCI class. In-hospital case fatality increased depending on the CCI class: 3.6% in very low, 6.5% in mild, 12.1% in moderate, and 22.1% in high CCI class (P < .001). CCI class was associated with increased in-hospital case fatality (odds ratio 2.014; 95% confidence interval, 2.000-2.027; P < .001). CONCLUSION: Our study results may help practitioners to better understand and measure the association between an aggravated comorbidity profile and increased in-hospital case fatality in patients with pulmonary embolism.

Perioperative Evaluation of the Physical Quality of Life of Patients with Non-Small Cell Lung Cancer: A Prospective Study.

Surgery is the most effective treatment for early-stage lung cancer; however, it poses a higher physical burden than other treatment options. Therefore, understanding the perioperative course of patients is important. Using the Short Form Health Survey 36, we prospectively measured the physical quality of life of patients who underwent anatomical pulmonary resection for non-small cell lung cancer at Shonan Kamakura General Hospital, Kanagawa, Japan (n = 87). In the preoperative setting, patients who had lower performance status and lived alone had significantly worse physical quality of life scores on multivariate analysis (regression coefficient (95% confidence interval), -9.37 (-13.43--5.32) and -10.22 (-13.74--7.40), respectively, p < 0.0001 for both). At 6 months postoperatively, patients who stopped smoking within 1 year preoperatively (stopped smoking within 1 year vs. remote or never smokers, 41.0 ± 10.5 vs. 48.6 ± 7.2, p = 0.002), had lower performance status (0 vs. 1-2, 49.3 ± 6.6 vs. 38.6 ± 9.6, p < 0.0001), lived alone (living alone vs. living with somebody, 41.6 ± 9.7 vs. 48.1 ± 7.9, p = 0.021), and had higher comorbid burden (Charlson comorbidity index <3 vs. ≥3, 48.2 ± 6.9 vs. 39.1 ± 14.7, p = 0.003) had significantly worse physical quality of life scores on univariate analysis. More recent smoking (regression coefficient (95% confidence interval), -4.90 (-8.78-1.0), p = 0.014), lower performance status (8.90 (5.10-12.70), p < 0.0001), living alone (5.76 (1.39-10.13), p = 0.01), and higher comorbid burden (-6.94 (-11.78--2.10), p = 0.006) were significant independent predictors of worse postoperative physical quality of life on multivariate analysis. Therefore, patients with these conditions might need additional support to maintain their physical condition after anatomical lung cancer surgery.

Charlson comorbidity index and the severity of community-acquired pneumonia caused by SARS-CoV-2: A retrospective analysis.

BACKGROUND: The Charlson comorbidity index (CCI) has been considered as a valid and reliable tool for predicting poor clinical outcomes and mortality in patients with coronavirus disease 2019 (COVID-19). However, its relationship with the severity of pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not been thoroughly explored. OBJECTIVES: The aim of the present study was to identify the impact of the comorbidity burden, quantitatively assessed by applying CCI, on the severity of inpatient community-acquired pneumonia (CAP) caused by SARS-CoV-2. MATERIAL AND METHODS: The study was conducted using the medical records of 208 patients with CAP who had an epidemiological history of a plausible SARS-CoV-2 infection, with positive polymerase chain reaction (PCR) confirmation no later than 1 month before being admitted for inpatient treatment. The CCI was calculated using a custom computer program. The statistical analysis of data was carried out using Statistica, v. 7.0. RESULTS: Our study found a significant correlation between the comorbidity burden and the severity of CAP caused by SARS-CoV-2. Specifically, we observed a low CCI score in the majority of patients in the pneumonia risk class II and III groups, and a high CCI score ≥3 in the majority of patients in the pneumonia risk class IV group. Moreover, a direct correlation between CCI and age was established. The comorbidities most commonly associated with CAP caused by SARS-CoV-2 were congestive heart failure, moderate to severe liver diseases and diabetes mellitus (DM) with chronic complications. CONCLUSIONS: The use of CCI to evaluate comorbid pathology in hospitalized patients with CAP caused by SARS-CoV-2 can assist the medical staff in developing timely preventive and therapeutic strategies, leading to improved patient prognosis.

¿Son las escalas de valoración de fragilidad superiores a la escala de valoración ASA en la predicción de complicaciones, estancia hospitalaria y readmisiones en prótesis total de rodilla? Un estudio comparativo entre pacientes octogenarios y septuagenarios / Are frailty scores superior to the ASA score in predicting complications, hospital stay, and readmissions in total knee replacement? A comparative study between octogenarian and septuagenarian patients

Antecedentes y objetivo: Las escalas de valoración de fragilidad no han sido estandarizadas para la evaluación prequirúrgica de pacientes sometidos a un reemplazo total de rodilla (RTR). El objetivo de este estudio fue comparar la eficacia de la escala de valoración de la Sociedad Americana de Anestesiología (ASA), el índice de comorbilidad de Charlson (ICC) y la escala simple de fragilidad (SSF) en la predicción de complicaciones, estancia hospitalaria, reingresos y mortalidad después del RTR electivo. Materiales y métodos: Estudiamos retrospectivamente a 448 pacientes que se sometieron a un RTR por artrosis en nuestra institución entre 2016 y 2019. Estos se dividieron en 2 grupos: grupo A (263 pacientes <80 años) y grupo B (185 pacientes >80 años). Todos fueron clasificados por escalas ASA, ICC y SSF. Resultados: El ICC fue mayor en el grupo B (mediana: 5 [RI: 4-6] vs. 4 [RI: 3-5]; p<0,001); sin embargo, no se asoció con un mayor número de complicaciones. Al realizar un análisis de regresión logística encontramos, para las complicaciones: OR SSF=0,67; ICC=1,11; ASA 3 y 4=0,89 y edad=1,04; mientras que para los reingresos: OR SSF=2,09; ICC=1,01; ASA 3 y 4=0,79 y edad=1. Conclusiones: Las escalas ICC y SSF demostraron no presentar diferencias a la escala ASA en la predicción de reingresos, complicaciones y estancia hospitalaria. Sin embargo, el SSF parece tener una mejor correlación en la predicción de la readmisión no planificada.(AU)

Background and objective: Frailty scores have not been standardized for the preoperative assessment of patients undergoing total knee replacement (TKR). The aim of this study was to compare the efficacy of the American Society of Anesthesiology (ASA) score, the Charlson comorbidity index (ICC) and the simple frailty score (SSF) in predicting complications, hospital stay, readmissions and mortality after elective TKR. Materials and methods: We retrospectively studied 448 patients who underwent TKR for osteoarthritis at our institution between 2016 and 2019. They were divided into two groups: Group A (263 patients, <80 years) and Group B (185 patients, >80 years).). All were classified by ASA, ICC and SSF scores. Results: The ICC was higher in Group B (median 5 [RI: 4-6] vs. 4 [RI: 3-5]; P<.001); however, it was not associated with a higher number of complications. When performing a logistic regression analysis we found, for complications: OR SSF=0.67, ICC=1.11; ASA 3 & 4=0.89 and age=1.04; while for readmissions: OR SSF=2.09; ICC=1.01; ASA 3 & 4=0.79 and age=1. Conclusions: The ICC and SSF scales showed no differences to the ASA scale in the prediction of readmissions, complications and hospital stay. However, the SSF seems to have a better correlation in predicting unplanned readmission.(AU)

[Translated article] Are frailty scores superior to the ASA score in predicting complications, hospital stay, and readmissions in total knee replacement? A comparative study between octogenarian and septuagenarian patients / ¿Son las escalas de valoración de fragilidad superiores a la escala de valoración ASA en la predicción de complicaciones, estancia hospitalaria y readmisiones en prótesis total de rodilla? Un estudio comparativo entre pacientes octogenarios y septuagenarios

Antecedentes y objetivo: Las escalas de valoración de fragilidad no han sido estandarizadas para la evaluación prequirúrgica de pacientes sometidos a un reemplazo total de rodilla (RTR). El objetivo de este estudio fue comparar la eficacia de la escala de valoración de la Sociedad Americana de Anestesiología (ASA), el índice de comorbilidad de Charlson (ICC) y la escala simple de fragilidad (SSF) en la predicción de complicaciones, estancia hospitalaria, reingresos y mortalidad después del RTR electivo. Materiales y métodos: Estudiamos retrospectivamente a 448 pacientes que se sometieron a un RTR por artrosis en nuestra institución entre 2016 y 2019. Estos se dividieron en 2 grupos: grupo A (263 pacientes <80 años) y grupo B (185 pacientes >80 años). Todos fueron clasificados por escalas ASA, ICC y SSF. Resultados: El ICC fue mayor en el grupo B (mediana: 5 [RI: 4-6] vs. 4 [RI: 3-5]; p<0,001); sin embargo, no se asoció con un mayor número de complicaciones. Al realizar un análisis de regresión logística encontramos, para las complicaciones: OR SSF=0,67; ICC=1,11; ASA 3 y 4=0,89 y edad=1,04; mientras que para los reingresos: OR SSF=2,09; ICC=1,01; ASA 3 y 4=0,79 y edad=1. Conclusiones: Las escalas ICC y SSF demostraron no presentar diferencias a la escala ASA en la predicción de reingresos, complicaciones y estancia hospitalaria. Sin embargo, el SSF parece tener una mejor correlación en la predicción de la readmisión no planificada.(AU)

Background and objective: Frailty scores have not been standardized for the preoperative assessment of patients undergoing total knee replacement (TKR). The aim of this study was to compare the efficacy of the American Society of Anesthesiology (ASA) score, the Charlson comorbidity index (ICC) and the simple frailty score (SSF) in predicting complications, hospital stay, readmissions and mortality after elective TKR. Materials and methods: We retrospectively studied 448 patients who underwent TKR for osteoarthritis at our institution between 2016 and 2019. They were divided into two groups: Group A (263 patients, <80 years) and Group B (185 patients, >80 years).). All were classified by ASA, ICC and SSF scores. Results: The ICC was higher in Group B (median 5 [RI: 4-6] vs. 4 [RI: 3-5]; P<.001); however, it was not associated with a higher number of complications. When performing a logistic regression analysis we found, for complications: OR SSF=0.67, ICC=1.11; ASA 3 & 4=0.89 and age=1.04; while for readmissions: OR SSF=2.09; ICC=1.01; ASA 3 & 4=0.79 and age=1. Conclusions: The ICC and SSF scales showed no differences to the ASA scale in the prediction of readmissions, complications and hospital stay. However, the SSF seems to have a better correlation in predicting unplanned readmission.(AU)