Aim: Modified-Krishnan’s frailty index (FI) is an FI calculation method developed by Krishnan et al. in 2014. This study aimed to compare the effectiveness and correlation of the FIs from Krishnan and the Canadian study of health and aging (CSHA) in predicting postoperative outcomes of elderly patients with hip fracture. Methods: Based on clinical follow-up and observation, we utilized these two instruments to predict 3-month mortality, hip function, and recovery of daily activities. The area under the curve (AUC) and the Pearson correlation coefficient were used to compare the two scales’ predictive validities for postoperative outcomes. Results: A total of 130 patients were included; 67% female and mean age 77.5 ± 8.5 years. The AUCs of modified-Krishnan’s FI (AUC = 0.856; 95% confidence interval (CI) = 0.767–0.945) and the CSHA-FI (AUC = 0.793; 95% CI = 0.652–0.934) were used to compare the effectiveness in predicting patient mortality. The optimal predictive scores were 0.335 and 0.28, respectively. The Pearson correlation analysis showed that the modified-Krishnan’s FI correlated with the Japanese Orthopaedic Association hip score (pain, activity, walking ability, and ability for daily living; R = −0.249, p = 0.005), while the CSHA-FI was not correlated ( R = −0.125, p = 0.170). The modified-Krishnan’s FI ( R = −0.415, p < 0.001) and the CSHA-FI ( R = −0.332, p < 0.001) were both significantly correlated with the functional recovery scale score. Conclusions: The modified-Krishnan’s FI and the CSHA-FI were effective in the prediction of postoperative mortality. But the modified-Krishnan’s FI was more consistently associated with the recovery of hip function and daily activities at 3 months after the operation than that of the CSHA-FI. The modified-Krishnan’s FI was more suitable to utilize for risk stratification, identifying deficits, and predicting recovery capacity in hip fracture patients.
Background A vast number of patients with chronic critical illness (CCI) have died of delayed organ failure in the intensive care unit (ICU). The weak organ function of patients needed appropriate tool to evaluate, which could provide reference for clinical decisions and communication with family members. The objective of this study was to develope and validate a prediction model for accurate, timely, simple, and objective identification of the critical degree of the patients' condition. Methods This study used a retrospective case–control and a prospective cohort study, with no interventions. Patients identified as CCI from a comprehensive ICU of a large metropolitan public hospital were selected. A total of 344 (case 172; control 172) patients were included to develop the Prognosis Prediction Model of Chronic Critical Illness (PPCCI Model) in this case-control study; 88 (case, 44; control 44) patients were included for the validation cohort in a prospective cohort study. The discrimination of the model was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC). Results The model comprised 9 predictors: age, prolonged mechanical ventilation (PMV), sepsis/other serious infections, Glasgow Coma Scale (GCS), mean artery pressure (MAP), heart rate (HR), respiratory rate (RR), oxygenation index (OI), and active bleeding.In both cohorts, the PPCCI Model could better identify the dead CCI patients (development cohort: AUC, 0.934; 95% CI, 0.908–0.960; validation cohort: AUC, 0.965; 95% CI, 0.931–0.999), and showed better discrimination than the Acute Physiology And Chronic Health Evaluation II (APACHE II), Modified Early Warning Score (MEWS), and Sequential Organ Failure Assessment (SOFA). Conclusions The PPCCI Model can provide a standardized measurement tool for ICU medical staff to evaluate the condition of CCI patients, to facilitate rational allocation of ward-monitoring resources or communicate with family members.
BACKGROUND: The evolution of critical care medicine and nursing has aided and enabled the rescue of a large number of patients from numerous life-threatening diseases. However, in many cases, patient health may not be quickly restored, and the long-term prognosis may not be optimistic. OBJECTIVES: In this study, we aimed to develop and validate a prediction model for accurate, precise, and objective identification of the severity of chronic critical illness (CCI) in patients. METHODS: We used a retrospective case-control and prospective cohort study with no interventions. Patients diagnosed with CCI admitted to the ICU of a large metropolitan public hospital were selected. In the case-control study, 344 patients (case: 172; control:172) were enrolled to develop the prognosis prediction model of chronic critical illness (PPCCI Model); 88 patients (case:44; control: 44) in a prospective cohort study, served as the validation cohort. The discrimination of the model was measured using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). RESULTS: Age, prolonged mechanical ventilation (PMV), sepsis or other severe infections, Glasgow Coma Scale (GCS), mean artery pressure (MAP), heart rate (HR), respiratory rate (RR), oxygenation index (OI), and active bleeding were the nine predictors included in the model. In both cohorts, the PPCCI model outperformed the Acute Physiology And Chronic Health Evaluation II (APACHE II), Modified Early Warning Score (MEWS), and Sequential Organ Failure Assessment (SOFA) in identifying deceased patients with CCI (development cohort: AUC, 0.934; 95%CI, 0.908–0.960; validation cohort: AUC, 0.965; 95% CI, 0.931–0.999). CONCLUSION: The PPCCI model can provide ICU medical staff with a standardized measurement tool for assessing the condition of patients with CCI, enabling them to allocate ward monitoring resources rationally and communicate with family members.
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