Introduction: A machine learning technique that imitates neural system and brain can provide better than traditional methods like logistic regression for survival prediction and create an algorithm by determining influential factors. Aim: To determine the influential factors on survival time of palliative care cancer patients and to compare two statistical methods for better prediction of survival. Methods: One-year data is gathered from the patients that we followed in the palliative care clinic of our hospital (2017-2018) (n = 189). All data were retrospectively evaluated. After descriptive statistics, we used Pearson and Spearman correlations for parametric and non-parametric variables. The Artificial Neural Networks (ANN) and logistic regression model were applied to parameters which have a significant correlation with short survival. Results: Significantly correlated variables with short survival were Palliative Performance Scale (PPS), Edmonton Symptom Assessment System (ESAS), Karnofsky Performance Scale (KPS), brain, liver, and distant metastasis, hemogram parameters, cero-reactive protein (CRP) and albumin (ALB). ANN model showed 89.3% prediction accuracy while the logistic regression model showed 73.0%. ANN model achieved a better AUC value of 0.86 than logistic regression model (0.76). Discussion: There are several prognostic evaluation tools such as PPS, KPS, CRP, albumin, leukocytes, neutrophil were reported several studies as survival-related parameters in logistic regression models, also. Many studies compare ANN with logistic regression. When we evaluated these parameters totally, we observed the same relations with survival then we used the same parameters in the ANN model. The effectivity of the survival prediction models can be improved with the use of ANN. Conclusion: ANN provides a more accurate estimation than logistic regression. ANN model is an important statistical method for survival prediction of cancer patients.
Abstract Pleural effusion is a frequently seen medical problem caused by pulmonary and non-pulmonary diseases. Spondylodiscitis is a very rare cause of pleural effusion and is typically diagnosed based on clinical, laboratory, microbiological and radiological findings. The low incidence and different clinical presentations of Spondylodiscitis make its diagnosis and treatment challenging. We present the case of a 78-year-old female who was initially admitted due to chest pain and, upon chest radiography, was found to have pleural effusion; and eventually diagnosed with spondylodiscitis. Keywords: Spondylodiscitis, exudative pleural effusion, geriatrics, vertebra, infection. Continuous...
Introduction: Palliative Performance Scale (PPS), Karnofsky Performance Scale (KPS) and the Edmonton Symptom Assessment Scale (ESAS) are widely used prognostic scales in palliative care unit. Institutionalized palliative care services in our country are quite new compared to the practices in many countries. We do not have concrete data on the compatibility of these scales, which are developed with patient data from other countries, in our own palliative care practices with real cases. In this study, we aimed to evaluate the convenience of the Turkish versions of these scales on the first days of hospitalization of terminal cancer patients who were followed up in the palliative clinic of our hospital. We also questioned whether the initial estimated survival and actual survival were compatible. Methods: PPS, KPS and ESAS on the first days of hospitalization of terminal cancer patients, hospitalized in the palliative clinic of our hospital between November 14, 2016 and November 14, 2017, were retrospectively evaluated one year later (n = 222). The survivors and those who lost their lives were determined. The survival estimates with PPS of the patients who died were compared with their actual survival. Results: The average age of 222 patients (18% female, 82% male) participating in the study was 64.49 ± 11.62, and the range was 26-91. PPS, KPS, ESAS were determined as a mean value of 34.40±18.00 (min. 10-max. 90), 32.90±17.50 (min. 10-max. 90), 56.10±15.65 (min. 2-max. 90), respectively. The AUC of PPS is 0.83 (p<0.001) and the AUC of KPS is 0.78 (p<0.001) suggesting that KPS and PPS has at least one tie between alive and dead patients, which is 45%. Median survival time was found 14.00, 95% C.I. [10.87-17.13]. Conclusion:In our study, it was found that as the KPS and PPS scores decrease, the survival time of terminal cancer patients decrease. Patients with PPS <45% had a higher risk of death (sensitivity 71%, specificity 80%), patients with KPS <45% had a higher risk of death (sensitivity 60%, specificity 81%), and patients with ESAS> 60.50 had a higher risk of death (sensitivity 93%, specificity 45%). PPS is a useful assessment scale for predicting survival of terminal cancer patients in the palliative care unit. We think it is important in determining our patient-specific palliative approach and treatments.
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