BackgroundThis study aimed to establish and validate an easy-to-use nomogram for predicting long-term mortality among ischemic stroke patients.MethodsAll raw data were obtained from the Medical Information Mart for Intensive Care IV database. Clinical features associated with long-term mortality (1-year mortality) among ischemic stroke patients were identified using least absolute shrinkage and selection operator regression. Then, binary logistic regression was used to construct a nomogram, the discrimination of which was evaluated by the concordance index (C-index), integrated discrimination improvement (IDI), and net reclassification index (NRI). Finally, a calibration curve and decision curve analysis (DCA) were employed to study calibration and net clinical benefit, compared to the Glasgow Coma Scale (GCS) and the commonly used disease severity scoring system.ResultsPatients who were identified with ischemic stroke were randomly assigned into developing (n = 1,443) and verification (n = 646) cohorts. The following factors were associated with 1-year mortality among ischemic stroke patients, including age on ICU admission, marital status, underlying dementia, underlying malignant cancer, underlying metastatic solid tumor, heart rate, respiratory rate, oxygen saturation, white blood cells, anion gap, mannitol injection, invasive mechanical ventilation, and GCS. The construction of the nomogram was based on the abovementioned features. The C-index of the nomogram in the developing and verification cohorts was 0.820 and 0.816, respectively. Compared with GCS and the commonly used disease severity scoring system, the IDI and NRI of the constructed nomogram had a statistically positive improvement in predicting long-term mortality in both developing and verification cohorts (all with p < 0.001). The actual mortality was consistent with the predicted mortality in the developing (p = 0.862) and verification (p = 0.568) cohorts. Our nomogram exhibited greater net clinical benefit than GCS and the commonly used disease severity scoring system.ConclusionThis proposed nomogram has good performance in predicting long-term mortality among ischemic stroke patients.
This study investigates a firm's return window decision in a distribution channel. Conventional wisdom posits that compared to a centralized channel, a decentralized channel induces worse services (e.g., a shorter return window) because of double marginalization. We build a model consisting of one manufacturer, one retailer, and consumers who are heterogeneous in their willingness‐to‐pay (WTP) for product quality. Counterintuitively, we find that the return window can be longer in a decentralized channel than in a centralized channel. Furthermore, the return window can decrease along with product quality, which means that high‐quality products can be offered shorter return windows than low‐quality products. When endogenizing product quality, we reveal, contrary to previous findings, that the product quality in a decentralized channel may be higher, even when consumer heterogeneity in product quality follows a uniform distribution. This higher product quality induces a decentralized channel to generate a consumer surplus higher than that of a centralized channel.
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