Model For Survival In Patients Research Articles

Prognostic model for survival in patients with early stage cervical cancer

In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer patients and to reconsider grounds for adjuvant treatment. A multivariate Cox regression model was used to identify the prognostic weight of clinical and histological factors for disease-specific survival (DSS) in 710 consecutive patients who had surgery for early stage cervical cancer (FIGO [International Federation of Gynecology and Obstetrics] stage IA2-IIA). Prognostic scores were derived by converting the regression coefficients for each prognostic marker and used in a score chart. The discriminative capacity was expressed as the area under the curve (AUC) of the receiver operating characteristic. The 5-year DSS was 92%. Tumor diameter, histological type, lymph node metastasis, depth of stromal invasion, lymph vascular space invasion, and parametrial extension were independently associated with DSS and were included in a Cox regression model. This prognostic model, corrected for the 9% overfit shown by internal validation, showed a fair discriminative capacity (AUC, 0.73). The derived score chart predicting 5-year DSS showed a good discriminative capacity (AUC, 0.85). In patients with early stage cervical cancer, DSS can be predicted with a statistical model. Models, such as that presented here, should be used in clinical trials on the effects of adjuvant treatments in high-risk early cervical cancer patients, both to stratify and to include patients.

Predictive Model for Survival in Patients With Advanced Cancer

To derive and validate a simple predictive model for survival of patients with metastatic cancer attending a palliative radiotherapy clinic. We described previously a model predicting survival of patients referred for palliative radiotherapy using six prognostic factors: primary cancer site, site of metastases, Karnofsky performance score (KPS), and the fatigue, appetite, and shortness of breath subscales from the Edmonton Symptom Assessment Scale. Here we simplified the model to include only three factors: primary cancer site, site of metastases, and KPS. Each factor was assigned a value proportional to its prognostic weight, and the weighted scores for each patient were summed to obtain a survival prediction score (SPS). Patients were also grouped according to their number of risk factors (NRF): nonbreast cancer, metastases other than bone, and KPS < or = 60. The three- and six- variable models were evaluated for their ability to predict survival in patients referred during a different time period and of those referred to a different cancer center. A training set of 395 patients, a temporal validation set of 445 patients, and an external validation set of 467 patients were used. The ability of the three- and six-variable models to separate patients into three prognostic groups and to predict their survival was similar using both SPS and NRF methods in the training, temporal, and external validation data sets. There was no statistically significant difference in the performance of the models. The three-variable NRF model is preferred because of its relative simplicity.

Recursive Partitioning Analysis of Prognostic Factors for Survival in Patients With Advanced Cancer

To construct a predictive model for survival of patients referred to the Rapid Response Radiotherapy Program using recursive partitioning (RP). We analyzed 16 factors characterizing patients with metastases at first referral to the Rapid Response Radiotherapy Program for palliative radiotherapy in 1999 for their effect on survival. They included age, primary cancer site, site of metastases, weight loss (>or=10% during the past 6 months), Karnofsky performance status (KPS), interval from the first diagnosis of cancer to the first consultation at the Rapid Response Radiotherapy Program, analgesic consumption within the previous 24 h, and the nine symptom scores from the Edmonton Symptom Assessment Scale. We used RP to develop a predictive model of survival for patients referred in 1999, followed by temporal validation using patients referred in 2000, and external validation using patients referred in 2002 to another institution. The model was able to separate patients into three groups with different durations of survival that were defined by (1) KPS >60; (2) KPS <or=60 with bone metastases only; and (3) KPS <or=60 with other metastases. The model performed moderately well when applied to the validation sets, but a major limitation was that it led to an unequal distribution of patients, with a small proportion of patients in the intermediate group. We have demonstrated that RP can be used to predict the survival of patients with advanced cancer. However, this model has no advantages compared with our published prognostic models that use the survival prediction scores and number of risk factors.

Validation of a Predictive Model for Survival in Metastatic Cancer Patients Attending an Outpatient Palliative Radiotherapy Clinic

To validate a predictive model for survival of patients attending a palliative radiotherapy clinic. We described previously a model that had good predictive value for survival of patients referred during 1999 (1). The six prognostic factors (primary cancer site, site of metastases, Karnofsky performance score, and the fatigue, appetite and shortness-of-breath items from the Edmonton Symptom Assessment Scale) identified in this training set were extracted from the prospective database for the year 2000. We generated a partial score whereby each prognostic factor was assigned a value proportional to its prognostic weight. The sum of the partial scores for each patient was used to construct a survival prediction score (SPS). Patients were also grouped according to the number of these risk factors (NRF) that they possessed. The probability of survival at 3, 6, and 12 months was generated. The models were evaluated for their ability to predict survival in this validation set with appropriate statistical tests. The median survival and survival probabilities of the training and validation sets were similar when separated into three groups using both SPS and NRF methods. There was no statistical difference in the performance of the SPS and NRF methods in survival prediction. Both the SPS and NRF models for predicting survival in patients referred for palliative radiotherapy have been validated. The NRF model is preferred because it is simpler and avoids the need to remember the weightings among the prognostic factors.

Low serum albumin levels and liver metastasis are powerful prognostic markers for survival in patients with carcinomas of unknown primary site

BACKGROUND. The authors investigated how lymphopenia and low serum albumin levels correlate with the prognosis of patients with carcinoma of unknown primary (CUP). METHODS. Univariate and multivariate prognostic factor analyses were conducted in a population of 317 consecutive patients with CUP who were evaluated at the Cross Cancer Institute of Edmonton, Alberta, Canada, from 1998 to 2004. RESULTS. The results from multivariate analysis showed that patients who had a performance status � 2 (using the World Health Organization scale), a high overall comorbidity score (on the Adult Comorbidity Evaluation 27), liver metastasis, elevated serum lactate dehydrogenase (LDH) levels, lymphopenia (defined as an absolute lymphocyte count � 0.7 � 10 9 /L), and low serum albumin levels had a worse prognosis. Based on the observation that the presence of liver metastasis and low serum albumin levels were the most powerful adverse prognostic factors, a classification scheme was delineated that took those 2 variables into account. A group of good-risk patients (no liver metastasis and normal serum albumin levels) and a group of poor-risk patients (liver metastasis and/or low serum albumin levels) were identified with median survivals of 371 days and 103 days, respectively (P < .0001). This classification was validated further in an independent data set of 124 patients who were evaluated at 2 French cancer centers: Among those patients, the median survival was 378 days in the good-risk group and 90 days in the poor-risk group (P < .0001). The new prognostic model substantially outperformed the previous standard prognostic model, which was based on performance status and serum LDH levels. CONCLUSIONS. Lymphopenia and low serum albumin levels were identified as 2 new independent markers of prognosis in patients with CUP. Although the authors confirmed the validity of the previous prognostic model, they developed and validated a more powerful, simple model based on the 2 most powerful adverse prognostic factors: liver metastasis and low serum albumin levels. These findings were confirmed in an independent cohort of patients with CUP, and consideration of the authors’ improved prognostic model for survival of patients with CUP is warranted. Cancer 2006:107:2698–705. � 2006 American Cancer Society.

Low serum albumin levels and liver metastasis are powerful prognostic markers for survival in patients with carcinomas of unknown primary site

The authors investigated how lymphopenia and low serum albumin levels correlate with the prognosis of patients with carcinoma of unknown primary (CUP). Univariate and multivariate prognostic factor analyses were conducted in a population of 317 consecutive patients with CUP who were evaluated at the Cross Cancer Institute of Edmonton, Alberta, Canada, from 1998 to 2004. The results from multivariate analysis showed that patients who had a performance status >/=2 (using the World Health Organization scale), a high overall comorbidity score (on the Adult Comorbidity Evaluation 27), liver metastasis, elevated serum lactate dehydrogenase (LDH) levels, lymphopenia (defined as an absolute lymphocyte count >/=0.7 x 10(9)/L), and low serum albumin levels had a worse prognosis. Based on the observation that the presence of liver metastasis and low serum albumin levels were the most powerful adverse prognostic factors, a classification scheme was delineated that took those 2 variables into account. A group of good-risk patients (no liver metastasis and normal serum albumin levels) and a group of poor-risk patients (liver metastasis and/or low serum albumin levels) were identified with median survivals of 371 days and 103 days, respectively (P < .0001). This classification was validated further in an independent data set of 124 patients who were evaluated at 2 French cancer centers: Among those patients, the median survival was 378 days in the good-risk group and 90 days in the poor-risk group (P < .0001). The new prognostic model substantially outperformed the previous standard prognostic model, which was based on performance status and serum LDH levels. Lymphopenia and low serum albumin levels were identified as 2 new independent markers of prognosis in patients with CUP. Although the authors confirmed the validity of the previous prognostic model, they developed and validated a more powerful, simple model based on the 2 most powerful adverse prognostic factors: liver metastasis and low serum albumin levels. These findings were confirmed in an independent cohort of patients with CUP, and consideration of the authors' improved prognostic model for survival of patients with CUP is warranted.

Validation and Extension of the Memorial Sloan-Kettering Prognostic Factors Model for Survival in Patients With Previously Untreated Metastatic Renal Cell Carcinoma

To validate the Motzer et al prognostic factors model for survival in patients with previously untreated metastatic renal cell carcinoma (RCC) and to identify additional independent prognostic factors. Data were collected on 353 previously untreated metastatic RCC patients enrolled onto clinical trials between 1987 and 2002. Four of the five prognostic factors identified by Motzer were independent predictors of survival. In addition, prior radiotherapy and presence of hepatic, lung, and retroperitoneal nodal metastases were found to be independent prognostic factors. Using the number of metastatic sites as surrogate for individual sites (none or one v two or three sites), Motzer's definitions of risk groups were expanded to accommodate these two additional prognostic factors. Using this expanded criteria, favorable risk is defined as zero or one poor prognostic factor, intermediate risk is two poor prognostic factors, and poor risk is more than two poor prognostic factors. According to Motzer's definitions, 19% of patients were favorable risk, 70% were intermediate risk, and 11% were poor risk; median overall survival times for these groups were 28.6, 14.6, and 4.5 months, respectively (P < .0001). Using the expanded criteria, 37% of patients were favorable risk, 35% were intermediate risk, and 28% were poor risk; median overall survival times of these groups were 26.0, 14.4, and 7.3 months, respectively (P < .0001). These data validate the model described by Motzer et al. Additional independent prognostic factors identified were prior radiotherapy and sites of metastasis. Incorporation of these additional prognostic factors into the Motzer et al model can help better define favorable risk, intermediate risk, and poor risk patients.

Probability prediction in multistate survival models for patients with chronic myeloid leukaemia.

In order to find an appropriate model suitable for a multistate survival experiment, 634 patients with chronic myeloid leukaemia (CML) were selected to illustrate the method of analysis. After transplantation, there were 4 possible situations for a patient: disease free, relapse but still alive, death before relapse, and death after relapse. The last 3 events were considered as treatment failure. The results showed that the risk of death before relapse was higher than that of the relapse, especially in the first year after transplantation with competing-risk method. The result of patients with relapse time less than 12 months was much poor by the Kaplan-Meier method. And the multistate survival models were developed, which were detailed and informative based on the analysis of competing risks and Kaplan-Meier analysis. With the multistate survival models, a further analysis on conditional probability was made for patients who were disease free and still alive at month 12 after transplantation. It was concluded that it was possible for an individual patient to predict the 4 possible probabilities at any time. Also the prognoses for relapse either death or not and death either before or after relapse may be given. Furthermore, the conditional probabilities for patients who were disease free and still alive in a given time after transplantation can be predicted.

Nomogram for Overall Survival of Patients With Progressive Metastatic Prostate Cancer After Castration

To develop a pretreatment prognostic model for survival of patients with progressive metastatic prostate cancer after castration using parameters that are measured during routine clinical management. Pretreatment clinical and biochemical determinants from 409 patients enrolled onto 19 consecutive therapeutic protocols from June 1989 through January 2000 were evaluated. The factors selected were age, Karnofsky performance status (KPS), hemoglobin (HGB), prostate-specific antigen (PSA), lactate dehydrogenase (LDH), alkaline phosphatase (ALK), and albumin. These factors were combined in an accelerated failure time regression model to produce a nomogram to predict median, 1-year, and 2-year survival. The nomogram was validated internally and externally using data from a multicenter randomized trial of suramin plus hydrocortisone versus hydrocortisone alone. The median survival of the entire group was 15.8 months (range, 0.9 to 77.8 months); 87% have died. In multivariable analysis, KPS, HGB, ALK, albumin, and LDH were significantly associated with survival (P <.05), whereas age and PSA were not. All seven factors were included in the nomogram. When applied to the external validation data set, the nomogram achieved a concordance index of 0.67. Calibration plots suggested that the nomogram was well calibrated for all predictions. A nomogram derived from pretreatment parameters that are measured on a routine basis was constructed. It can be used to predict the median, 1-year, and 2-year survival of patients with progressive castrate metastatic disease with reasonable accuracy. The information is useful to assess prognosis, guide treatment selection, and design clinical trials.

Artificial Neural Network Model of Survival in Patients Treated With Irradiation With and Without Concurrent Chemotherapy for Advanced Carcinoma of the Head and Neck

Purpose: This study was performed to investigate the feasibility of predicting survival in squamous cell carcinoma of the head and neck (SCCHN) with an artificial neural network (ANN), and to compare ANN performance with conventional models. Methods and Materials: Data were analyzed from a Phase III trial in which patients with locally advanced SCCHN received hyperfractionated irradiation with or without concurrent cisplatin and 5-fluorouracil. Of the 116 randomized patients, 95 who had 2-year follow-up and all required data were evaluated. ANN and logistic regression (LR) models were constructed to predict 2-year total survival using round-robin cross-validation. A modified staging model was also examined. Results: The best LR model used tumor size, nodal stage, and race to predict survival. The best ANN used nodal stage, tumor size, stage, and resectability, and hemoglobin. Treatment type did not predict 2-year survival and was not included in either model. Using the respective best feature sets, the area under the receiver operating characteristic curve (A z) for the ANN was 0.78 ± 0.05, showing more accurate overall performance than LR (A z = 0.67 ± 0.05, p = 0.07). At 70% sensitivity, the ANN was 72% specific, while LR was 54% specific ( p = 0.08). At 70% specificity, the ANN was 72% sensitive, while LR was 54% sensitive ( p = 0.07). When both models used the five predictive variables best for an ANN, A z for LR decreased [A z = 0.61 ± 0.06, p < 0.01 (ANN)]. The models performed equivalently when using the three variables best for LR. The best ANN also compared favorably with staging [A z = 0.60 ± 0.07, p = 0.02 (ANN)]. Conclusions: An ANN modeled 2-year survival in this data set more accurately than LR or staging models and employed predictive variables that could not be used by LR. Further work is planned to confirm these results on larger patient samples, examining longer follow-up to incorporate treatment type into the model.