A Simple and Cheap Hospitalization Risk Assessment Tool for Use in Hemodialysis Patients

Objective: To develop a simple, objective, cheap scoring tool incorporating nutritional parameters and other variables to predict hospitalization and mortality among hemodialysis patients – a tool that could be utilized in low resource countries. Methods: The following variables were scored according to severity into 0, 1, 2 or 3: BMI, functional capacity, HD vintage in years, serum albumin, serum ferritin, and the number of comorbid conditions (diabetes mellitus, hypertension, ischemic heart disease, cerebrovascular disease). This tool was evaluated on our regular hemodialysis patients who were followed up for 24 months (June 2015 till July 2017). In our study population, the maximum score recorded was 12; accordingly, a score of 6 was used to differentiate between a low-risk group (score < 6) or a high-risk group (score ≥6). The 2 groups were compared (using the Chi square test) for possible differences in mortality and hospitalization rates during the follow-up period. Results: One hundred and forty adult hemodialysis patients were monitored over 2 years; 83 were males and 57 females; 59% of the patients had diabetes mellitus. Twenty-nine patients (30.7%) were found to be in the high-risk group and 111 (79.3%) in the low-risk group. The high-risk patients were almost one and a half times more likely to be hospitalized for vascular access issues than the low-risk group (p = 0.056) and 3 times more likely to be hospitalized for non-vascular access issues than the low-risk group (p = 0.0001). The mortality rate in the high-risk group was 3.1 times that in the low-risk group, but this was not statistically significant. Conclusion: Using a simple and cheap assessment tool in hemodialysis patients, we have identified patients at high risk for hospitalization rates and mortality. Video Journal Club “Cappuccino with Claudio Ronco” at http://www.karger.com/?doi=490544.

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Adjuvant Chemotherapy Guidance for pT1-3N0-1 Breast Cancer Patients with HR+, HER2- subtype: a study based on SEER database

10.21203/rs.3.rs-154913/v1 ◽

2021 ◽

Author(s):

juanjuan Qiu ◽

Li Xu ◽

Yu Wang ◽

Jia Zhang ◽

Jiqiao Yang ◽

...

Keyword(s):

Breast Cancer ◽

Prognostic Factors ◽

High Risk ◽

Cancer Patients ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Rank Test ◽

Breast Cancer Patients ◽

Group Score

Abstract Background Although the results of gene testing can guide early breast cancer patients with HR+, HER2- to decide whether they need chemotherapy, there are still many patients worldwide whose problems cannot be solved well by genetic testing. Methods 144 735 patients with HR+, HER2-, pT1-3N0-1 breast cancer from the Surveillance, Epidemiology, and End Results database were included from 2010 to 2015. They were divided into chemotherapy (n = 38 392) and no chemotherapy (n = 106 343) group, and after propensity score matching, 23 297 pairs of patients were left. Overall survival (OS) and breast cancer-specific survival (BCSS) were tested by Kaplan–Meier plot and log-rank test and Cox proportional hazards regression model was used to identify independent prognostic factors. A nomogram was constructed and validated by C-index and calibrate curves. Patients were divided into high- or low-risk group according to their nomogram score using X-tile. Results Patients receiving chemotherapy had better OS before and after matching (p < 0.05) but BCSS was not significantly different between patients with and without chemotherapy after matching: hazard ratio (HR) 1.005 (95%CI 0.897, 1.126). Independent prognostic factors were included to construct the nomogram to predict BCSS of patients without chemotherapy. Patients in the high-risk group (score > 238) can get better OS HR 0.583 (0.507, 0.671) and BCSS HR 0.791 (0.663, 0.944) from chemotherapy but the low-risk group (score ≤ 238) cannot. Conclusion The well-validated nomogram and a risk stratification model was built. Patients in the high-risk group should receive chemotherapy while patients in low-risk group may be exempt from chemotherapy.

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Risk scoring model to predict recurrence in locally advanced breast cancer (LABC) treated with neoadjuvant chemotherapy (NACT).

Journal of Global Oncology ◽

10.1200/jgo.2019.5.suppl.98 ◽

2019 ◽

Vol 5 (suppl) ◽

pp. 98-98

Author(s):

Sushma Agrawal ◽

Prabhakar Mishra ◽

Punita Lal ◽

Gaurav Agarwal ◽

Amit Agarwal ◽

...

Keyword(s):

Risk Factors ◽

High Risk ◽

Risk Group ◽

Locally Advanced ◽

High Risk Group ◽

Low Risk ◽

Scoring Model ◽

Group Score ◽

Risk Scoring ◽

Independent Risk Factors

98 Background: Complete response (CR) to NACT portends favorable long term outcomes in LABC. There is a need for a tool to risk categorise patients for recurrence risk (RR), so that intensification of treatment can be offered to women with high risk of recurrence. Methods: A prospectively maintained database of LABC (between January 2007 to December 2012), who received NACT followed by definitive surgery, radiotherapy and endocrine therapy in endocrine sensitive disease was retrospectively analyzed for clinico-pathological and treatment factors affecting disease free survival (DFS). A risk scoring model was developed on the basis of beta coefficients of identified independent risk factors for DFS. Results: The incidence of loco-regional relapse was 8% and that of distant metastases was 32% in a dataset of 206 patients at a median follow-up of 47 months (IQR 24-62 mo). The independent risk factors for recurrence were index T stage [HR 1.8 (0.9-3.6)], N stage [HR 1.7 (0.4 – 4.7)], grade [HR 1.8 (0.8-4.2)], age less than and more than 40 years [HR 1.6 (0.4-0.9)], pathologic CR [HR 4.3 (1.7- 10.7)], intrinsic subtype [HR 2.2 (1.3-3.7)], and type of surgery (BCS vs MRM) [HR 2.2 (1.3-3.6)]. The ROC of the model for the prediction of recurrence was 0.67 (95 % CI: 0.61-0.75). The results of this model were validated by dividing the population into 3 risk groups: low risk (score less than 12), intermediate risk group (score between 13-15), high risk group (score 16 or more). The chances of recurrence are 16% versus 34% versus 57% in low, intermediate and high risk group respectively. Presence of three risk factors implies low risk, five intermediate and more than five high risk. Conclusions: The risk scoring model developed by us predicts RR and can be used for selecting patients for treatment intensification in high risk category.

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Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool

Journal of Neurosurgery Spine ◽

10.3171/2016.12.spine16969 ◽

2017 ◽

Vol 27 (1) ◽

pp. 81-91 ◽

Cited By ~ 37

Author(s):

Anand Veeravagu ◽

Amy Li ◽

Christian Swinney ◽

Lu Tian ◽

Adrienne Moraff ◽

...

Keyword(s):

Risk Assessment ◽

High Risk ◽

Risk Stratification ◽

Spine Surgery ◽

Assessment Tool ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Acs Nsqip ◽

Medium Risk

OBJECTIVEThe ability to assess the risk of adverse events based on known patient factors and comorbidities would provide more effective preoperative risk stratification. Present risk assessment in spine surgery is limited. An adverse event prediction tool was developed to predict the risk of complications after spine surgery and tested on a prospective patient cohort.METHODSThe spinal Risk Assessment Tool (RAT), a novel instrument for the assessment of risk for patients undergoing spine surgery that was developed based on an administrative claims database, was prospectively applied to 246 patients undergoing 257 spinal procedures over a 3-month period. Prospectively collected data were used to compare the RAT to the Charlson Comorbidity Index (CCI) and the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator. Study end point was occurrence and type of complication after spine surgery.RESULTSThe authors identified 69 patients (73 procedures) who experienced a complication over the prospective study period. Cardiac complications were most common (10.2%). Receiver operating characteristic (ROC) curves were calculated to compare complication outcomes using the different assessment tools. Area under the curve (AUC) analysis showed comparable predictive accuracy between the RAT and the ACS NSQIP calculator (0.670 [95% CI 0.60–0.74] in RAT, 0.669 [95% CI 0.60–0.74] in NSQIP). The CCI was not accurate in predicting complication occurrence (0.55 [95% CI 0.48–0.62]). The RAT produced mean probabilities of 34.6% for patients who had a complication and 24% for patients who did not (p = 0.0003). The generated predicted values were stratified into low, medium, and high rates. For the RAT, the predicted complication rate was 10.1% in the low-risk group (observed rate 12.8%), 21.9% in the medium-risk group (observed 31.8%), and 49.7% in the high-risk group (observed 41.2%). The ACS NSQIP calculator consistently produced complication predictions that underestimated complication occurrence: 3.4% in the low-risk group (observed 12.6%), 5.9% in the medium-risk group (observed 34.5%), and 12.5% in the high-risk group (observed 38.8%). The RAT was more accurate than the ACS NSQIP calculator (p = 0.0018).CONCLUSIONSWhile the RAT and ACS NSQIP calculator were both able to identify patients more likely to experience complications following spine surgery, both have substantial room for improvement. Risk stratification is feasible in spine surgery procedures; currently used measures have low accuracy.

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Predictive ability of the osteoporosis self-assessment tool for assessing the risk of osteoporosis

10.35465/27.3.2019.pp3-9 ◽

2019 ◽

Keyword(s):

High Risk ◽

Assessment Tool ◽

Risk Group ◽

Osteoporotic Fractures ◽

High Risk Group ◽

Low Risk ◽

Moderate Risk ◽

Self Assessment ◽

Number Of Patients ◽

Increased Risk

Osteoporosis (OP) is a progressive metabolic bone disease caused by disturbed balance between bone formation and bone resorption. Osteoporotic fractures lead to a deterioration in the quality of patients’ life due to high morbidity and mortality, and the economic burden of osteoporotic fractures is expected to increase. Various tools have been developed to assess the risk of osteoporosis in the clinical practice. The Osteoporosis Self-Assessment Tool (OST) is used to predict osteoporosis and is suitable for self-assessment. The purpose of this study is to assess the ability of the OST score to predict the risk of OP. 180 postmenopausal women with a mean age of 61 ± 13 years (38-86 years) were included in the study. The OST score was evaluated using the formula: (body weight  age) × 0.2. Patients were divided into three groups according to the risk of OP: low risk (> -1), moderate risk (-1 to -4) and high risk (<-4). Based on the total lumbar spine T-score, measured by dual-energy X-ray absorptiometry (DEXA), the actual number of the women with OP was established. According to the OST score, 22 women were in the high risk group, 41 women in the moderate risk group, and 117 women in the low risk group. There was a correlation between the risk of OP calculated with OST and the number of patients with OP, established by DEXA measurement - with increased risk of OP, the number of the women with OP also increased (p = 0.000). The percentage of the women with osteoporosis is highest in the high risk group and lowest in the low risk group. In the high risk group, 95.5% of the women had a diagnosis of osteoporosis. These results demonstrate the good ability of OST score to predict the risk of OP in the Bulgarian population.

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23. Development and Validation of a Risk Score for Post-transplant Lymphoproliferative Disorders among Solid Organ Transplant Recipients

Open Forum Infectious Diseases ◽

10.1093/ofid/ofab466.023 ◽

2021 ◽

Vol 8 (Supplement_1) ◽

pp. S15-S16

Author(s):

Quenia dos Santos ◽

Neval E Wareham ◽

Amanda Mocroft ◽

Allan Rasmussen ◽

Finn Gustafsson ◽

...

Keyword(s):

High Risk ◽

Risk Score ◽

Risk Group ◽

Organ Transplant ◽

High Risk Group ◽

Low Risk ◽

Solid Organ ◽

Group Score ◽

Barr Virus ◽

Epstein Barr

Abstract Background Post-transplant lymphoproliferative disease (PTLD) is a well-recognized complication after transplant. This study aimed to develop and independently validate a risk score to predict PTLD among solid organ transplant (SOT) recipients (kidney, liver, lung and heart). Methods Poisson regression identified predictors of PTLD with the best fitting model selected for the risk score, where each predictor contributed with a risk coefficient to the risk score, dividing patients in high vs low risk of having a PTLD. Results For both cohorts, most of the patients were male, aged more than 16 years old, kidney recipients and with a low-risk pre-transplant Epstein-Barr Virus (EBV) IgG donor/recipient serostatus. The derivation cohort consisted of 2546 SOT transplanted at Rigshospitalet, Copenhagen between 2004-2019; 57 developed PTLD. Predictors of PTLD were high-risk pre-transplant Epstein-Barr Virus (EBV) IgG donor/recipient serostatus, and current plasma EBV DNA positive, abnormal hemoglobin and C-reactive protein levels. A positive EBV DNA was the strongest parameter for the PTLD risk score (figure 1), although the model was able to predict the risk of PTLD cases in both EBV positive and EBV negative individuals. Individuals in the high-risk group had almost 7 times higher incidence of PTLD compared to the low risk group (table 1). In the validation cohort of 1611 SOT recipients between 2008-2018 from University Hospital of Zürich, 24 developed PTLD. A similar seven times higher risk of PTLD was observed in the high-risk group compared to the low risk group (table 1). The discriminatory ability was also similar in derivation (Harrell’s C-statistic of 0.82 95%CI (0.76-0.88) and validation (0.82, 95% CI:0.72-0.92) cohorts. An explanation about how the risk for PTLD is calculated for the SOT recipients; in this example the risk of developing PTLD is calculated in the next 180 days Performance of the PTLD score in the derivation and validation cohorts (low-risk group: score<=17 points; high-risk group: score>17 points) Conclusion The risk score had a good discriminatory ability in both cohorts and helped to identify patients with higher risk of developing PTLD, so they can be monitored more often. This is the first risk-score developed and externally validated to predict risk of PTLD among SOT recipients. Disclosures All Authors: No reported disclosures

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Echocardiography and EuroSCORE II for the stratification of low-gradient severe aortic stenosis and preserved left ventricular ejection fraction

The International Journal of Cardiovascular Imaging ◽

10.1007/s10554-021-02373-2 ◽

2021 ◽

Author(s):

Yan Fan ◽

Hong Shen ◽

Brandon Stacey ◽

David Zhao ◽

Robert J. Applegate ◽

...

Keyword(s):

High Risk ◽

Left Ventricular Ejection Fraction ◽

Risk Group ◽

Severe Aortic Stenosis ◽

Left Ventricular ◽

High Risk Group ◽

Low Risk ◽

Left Ventricular Ejection ◽

Ventricular Ejection Fraction ◽

Risk Patients

AbstractThe purpose of this study was to explore the utility of echocardiography and the EuroSCORE II in stratifying patients with low-gradient severe aortic stenosis (LG SAS) and preserved left ventricular ejection fraction (LVEF ≥ 50%) with or without aortic valve intervention (AVI). The study included 323 patients with LG SAS (aortic valve area ≤ 1.0 cm2 and mean pressure gradient < 40 mmHg). Patients were divided into two groups: a high-risk group (EuroSCORE II ≥ 4%, n = 115) and a low-risk group (EuroSCORE II < 4%, n = 208). Echocardiographic and clinical characteristics were analyzed. All-cause mortality was used as a clinical outcome during mean follow-up of 2 ± 1.3 years. Two-year cumulative survival was significantly lower in the high-risk group than the low-risk patients (62.3% vs. 81.7%, p = 0.001). AVI tended to reduce mortality in the high-risk patients (70% vs. 59%; p = 0.065). It did not significantly reduce mortality in the low-risk patients (82.8% with AVI vs. 81.2%, p = 0.68). Multivariable analysis identified heart failure, renal dysfunction and stroke volume index (SVi) as independent predictors for mortality. The study suggested that individualization of AVI based on risk stratification could be considered in a patient with LG SAS and preserved LVEF.

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SARS-CoV-2 Seroprevalence in Healthcare Workers in Germany: A Follow-Up Study

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18094540 ◽

2021 ◽

Vol 18 (9) ◽

pp. 4540

Author(s):

Johannes Korth ◽

Benjamin Wilde ◽

Sebastian Dolff ◽

Jasmin Frisch ◽

Michael Jahn ◽

...

Keyword(s):

High Risk ◽

Healthcare Workers ◽

Risk Group ◽

Close Contact ◽

Virus Transmission ◽

High Risk Group ◽

Low Risk ◽

University Hospital ◽

Intermediate Risk ◽

Igg Antibody

SARS-CoV-2 is a worldwide challenge for the medical sector. Healthcare workers (HCW) are a cohort vulnerable to SARS-CoV-2 infection due to frequent and close contact with COVID-19 patients. However, they are also well trained and equipped with protective gear. The SARS-CoV-2 IgG antibody status was assessed at three different time points in 450 HCW of the University Hospital Essen in Germany. HCW were stratified according to contact frequencies with COVID-19 patients in (I) a high-risk group with daily contacts with known COVID-19 patients (n = 338), (II) an intermediate-risk group with daily contacts with non-COVID-19 patients (n = 78), and (III) a low-risk group without patient contacts (n = 34). The overall seroprevalence increased from 2.2% in March–May to 4.0% in June–July to 5.1% in October–December. The SARS-CoV-2 IgG detection rate was not significantly different between the high-risk group (1.8%; 3.8%; 5.5%), the intermediate-risk group (5.1%; 6.3%; 6.1%), and the low-risk group (0%, 0%, 0%). The overall SARS-CoV-2 seroprevalence remained low in HCW in western Germany one year after the outbreak of COVID-19 in Germany, and hygiene standards seemed to be effective in preventing patient-to-staff virus transmission.

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Sernbo score predicts survival after intracapsular hip fracture in the elderly

Annals of The Royal College of Surgeons of England ◽

10.1308/003588413x13511609954653 ◽

2013 ◽

Vol 95 (1) ◽

pp. 29-33 ◽

Cited By ~ 20

Author(s):

EJC Dawe ◽

E Lindisfarne ◽

T Singh ◽

I McFadyen ◽

P Stott

Keyword(s):

Hip Fracture ◽

High Risk ◽

Risk Group ◽

Characteristic Curve ◽

Social Situation ◽

High Risk Group ◽

Low Risk ◽

Risk Of Death ◽

Intracapsular Hip Fracture ◽

The Mean

Introduction The Sernbo score uses four factors (age, social situation, mobility and mental state) to divide patients into a high-risk and a low-risk group. This study sought to assess the use of the Sernbo score in predicting mortality after an intracapsular hip fracture. Methods A total of 259 patients with displaced intracapsular hip fractures were included in the study. Data from prospectively generated databases provided 22 descriptive variables for each patient. These included operative management, blood tests and co-mobidities. Multivariate analysis was used to identify significant predictors of mortality. Results The mean patient age was 85 years and the mean follow-up duration was 1.5 years. The one-year survival rate was 92% (±0.03) in the low-risk group and 65% (±0.046) in the high-risk group. Four variables predicted mortality: Sernbo score >15 (p=0.0023), blood creatinine (p=0.0026), ASA (American Society of Anaesthesiologists) grade >3 (p=0.0038) and non-operative treatment (p=0.0377). Receiver operating characteristic curve analysis showed the Sernbo score as the only predictor of 30-day mortality (area under curve 0.71 [0.65–0.76]). The score had a sensitivity of 92% and a specificity of 51% for prediction of death at 30 days. Conclusions The Sernbo score identifies patients at high risk of death in the 30 days following injury. This very simple score could be used to direct extra early multidisciplinary input to high-risk patients on admission with an intracapsular hip fracture.

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Abstract 2126: Twoaces (tia Work-up Asoutpatient: Assessment Of Clincal Efficacy And Safety)

Stroke ◽

10.1161/str.43.suppl_1.a2126 ◽

2012 ◽

Vol 43 (suppl_1) ◽

Author(s):

Jenifer Green ◽

Connie Wolford ◽

Jean Marc Olivot ◽

Gregory Albers ◽

James Castle

Keyword(s):

High Risk ◽

Observational Study ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Stroke Rate ◽

Significant Stenosis ◽

Non Invasive ◽

Low Rate ◽

Arterial Imaging

Background: Much controversy exists as to which TIA patients need to be admitted to the hospital for evaluation and treatment and which can be sent home. One commonly used trigae tool is the ABCD 2 score (Age, presenting Blood Pressure, Clinical symptoms and Duration, and Diabetes). Although this tool gives good information for determining populations at low risk (score of 0-3) and high risk (score of 6-7) of stroke after TIA, it leaves a large moderate risk population (score of 4-5) for whom no clear triage guidance can be given. As previous studies have found large artery atherosclerosis to be a potent risk factor for stroke after TIA, we attempted to further delineate low and high risk TIA populations with the addition of non-invasive arterial imaging to the ABCD 2 score. Methods: All patients referred to the Stanford Stroke Service for possible TIA within 72 hrs of symptom onset between July 2007 and February 2010, and all patients referred to the Highland Park Stroke Service for possible TIA within 72 hrs of symptom onset after October 2009 were screened for enrollment in this observational study. Exclusion criteria included age <18 years, use of TPA at initial presentation, and symptoms lasting >24 hours. 352 patients were invited to enroll, 3 refused. Of the 349 enrolled, follow-up was obtained in 346 patients at 30 days. Patients were placed into two groups: 1) those with ABCD 2 scores of 0-3 or scores of 4-5 AND no sign of hemodynamically significant stenosis in an artery within the distribution of the TIA (Low Risk Group); and 2) those with ABCD 2 scores of 6-7 or scores of 4-5 AND a hemodynamically significant stenosis in an artery within the distribution of the TIA (High Risk Group). Non-invasive arterial imaging included CT angiogram, MR angiogram, and carotid ultrasound - all used at the discretion of the treating physician. 30 day stroke rates with 95% confidence intervals were recorded. Results: Of the 346 patients enrolled, 295 (85.3%) fell into the "Low Risk Group" based on ABCD 2 scoring and non-invasive arterial imaging. Within that group, the stroke rate at 30 days was 1.0% (3 strokes, 95% CI 0.2-3.1%). Within the "High Risk Group", the stroke rate at 30 days was 5.9% (3 strokes, 95% CI 1.4-16.5%). Within the "Low Risk Group", all 3 of the strokes occurred in patients with ABCD 2 scores of 4-5 (3/133 patients - 2.3% stroke rate with 95% CI 0.5-6.7%). The overall stroke rate was 6/346 (1.7%, 95% CI 0.7-3.8%). Conclusions: In our observational study we found that the overall 30 day stroke rate after TIA was quite low. The percentage of all TIA patients falling into the “Low Risk Group” was quite high, and these patients had a particularly low rate of stroke at 30 days. Given the high number of "Low Risk" patients and the low rate of stroke in that group at 30 days, the vast majority of TIA patients could likely be safely evaluated in an rapid outpatient setting provided that the treating physician is confident of the diagnosis.

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Accuracy of the CARG chemotherapy toxicity risk prediction tool in older Indian patients with cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e24023 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e24023-e24023

Author(s):

Shreya Gattani ◽

Vanita Noronha ◽

Anant Ramaswamy ◽

Renita Castelino ◽

Vandhita Nair ◽

...

Keyword(s):

High Risk ◽

Cancer Patients ◽

Risk Prediction ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Chemotherapy Toxicity ◽

Patients With Cancer ◽

Indian Patients ◽

Grade 3

e24023 Background: Clinical judgement alone is inadequate in accurately predicting chemotherapy toxicity in older adult cancer patients. Hurria and colleagues developed and validated, the CARG score (range, 0–17) as a convenient and reliable tool for predicting chemotherapy toxicity in older cancer patients in America, however, its applicability in Indian patients is unknown. Methods: An observational retrospective and prospective study between 2018 and 2020 was conducted in the Department of Medical Oncology at Tata Memorial Hospital, Mumbai, India. The study was approved by the institutional ethics committee (IEC-III; Project No. 900596) and registered in the Clinical Trials Registry of India (CTRI/2020/04/024675). Written informed consent was obtained in the prospective part of the study. Patients aged ≥ 60 years and planned for systemic therapy were evaluated in the geriatric oncology clinic and their CARG score was calculated. Patients were stratified into low (0-4), intermediate (5-9) and high risk (10-17) based on the CARG scores. The CARG score was provided to the treating physicians, along with the results of the geriatric assessment. Chemotherapy-related toxicities were captured from the electronic medical record and graded as per the NCI CTCAE, version 4.0. Results: We assessed 130 patients, with a median age 69 years (IQR, 60 to 84); 72% patients were males. The common malignancies included gastrointestinal (52%) and lung (30%). Approximately 78% patients received polychemotherapy and 53% received full dose chemotherapy. Based on the CARG score, 28 (22%) patients belonged to low risk, 80 (61%) to intermediate risk and 22 (17%) to the high risk category. The AU-ROC of the CARG score in predicting grade 3-5 toxicities was 0.61 (95% CI, 0.51-0.71). The sensitivity and specificity of the CARG score in predicting grade 3-5 toxicities were 60.8% and 78.6%. Grade 3-5 toxicities occurred in 6/28 patients (21%) in the low risk group, compared to 62/102 patients (61%) in the intermediate /high risk group, p = 0.0002. There was also a significant difference in the time to development of grade 3-5 toxicities, which occurred at a median of 2.5 cycles (IQR, 1-3.8) in the intermediate /high risk group and at a median of 6 cycles (IQR, 3.5-8) in the low risk group, p = 0.0011. Conclusions: In older Indian patients with cancer, the CARG score reliably stratifies patients into low risk and intermediate/high risk categories, predicting both the occurrence and the time to occurrence of grade 3-5 toxicities from chemotherapy. The CARG score may aid the oncologist in estimating the risk-benefit ratio of chemotherapy. An important limitation was that we provided the CARG score to the treating oncologists prior to the start of chemotherapy, which may have resulted in alterations in the chemotherapy regimen and dose and may have impacted the CARG risk prediction model. Clinical trial information: CTRI/2020/04/024675.

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