key: cord-0029972-x96nza0o authors: Lu, Yan; Zhang, Qiaohong; Jiang, Jinwen title: Development and validation of a prediction model for in-hospital mortality of patients with severe thrombocytopenia date: 2022-04-15 journal: Sci Rep DOI: 10.1038/s41598-022-10438-y sha: f1c866fdac75ef39988759a413de8fcc0e044941 doc_id: 29972 cord_uid: x96nza0o Risk stratification and prognosis evaluation of severe thrombocytopenia are essential for clinical treatment and management. Currently, there is currently no reliable predictive model to identify patients at high risk of severe thrombocytopenia. This study aimed to develop and validate a prognostic nomogram model to predict in-hospital mortality in patients with severe thrombocytopenia in the intensive care unit. Patients diagnosed with severe thrombocytopenia (N = 1561) in the Medical Information Mart for Intensive Care IV database were randomly divided into training (70%) and validation (30%) cohorts. In the training cohort, univariate and multivariate logistic regression analyses with positive stepwise selection were performed to screen the candidate variables, and variables with p < 0.05 were included in the nomogram model. The nomogram model was compared with traditional severity assessment tools and included the following 13 variables: age, cerebrovascular disease, malignant cancer, oxygen saturation, heart rate, mean arterial pressure, respiration rate, mechanical ventilation, vasopressor, continuous renal replacement therapy, prothrombin time, partial thromboplastin time, and blood urea nitrogen. The nomogram was well-calibrated. According to the area under the receiver operating characteristics, reclassification improvement, and integrated discrimination improvement, the nomogram model performed better than the traditional sequential organ failure assessment (SOFA) score and simplified acute physiology score II (SAPS II). Additionally, according to decision curve analysis, a threshold probability between 0.1 and 0.75 indicated that our constructed nomogram model showed more net benefits than the SOFA score and SAPS II. The nomogram model we established showed superior predictive performance and can assist in the quantitative assessment of the prognostic risk in patients with severe thrombocytopenia. www.nature.com/scientificreports/ L 1 . However, increased platelet consumption, impaired production, or severe destruction may lead to varying degrees of platelet reduction 2 . To date, there is no consistent definition of thrombocytopenia. Generally, thrombocytopenia is classified into mild (100-149 × 10 9 /L), moderate (50-99 × 10 9 /L) and severe (< 50 × 10 9 /L), according to the absolute number of platelets 3 . Thrombocytopenia is common in critically ill patients. Studies have found that more than 60% of intensive care unit (ICU) patients develop thrombocytopenia 4 , with severe thrombocytopenia occurring in 2.3-27% of them 5 As the most common cause of hemostatic disorders in ICUs, thrombocytopenia is associated with a higher risk of bleeding and blood transfusion 6 . Although the mechanisms leading to thrombocytopenia vary, severe thrombocytopenia indicates a worse clinical prognosis in critically ill patients 7, 8 . Identifying risk factors for mortality in patients with severe thrombocytopenia can help clinicians develop individualized treatment strategies and enhanced care management to reduce the risk of adverse events and improve patient outcomes. Thus, risk stratification and prognosis evaluation of patients with severe thrombocytopenia are essential for clinical treatment and management. Currently, there is currently no reliable predictive model that can be used to identify patients at a high risk of severe thrombocytopenia. The visual nomogram is an intuitive and easy-to-use predictive tool whose performance has been validated in predictive models for multiple diseases 9, 10 . This study aimed to develop and verify a nomogram model to predict mortality in patients with severe thrombocytopenia during ICU admission based on a large dataset of diagnosis and treatment from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Patient characteristics. In total, 1,561 patients with severe thrombocytopenia were enrolled in this study and randomly divided into a training cohort of 1,093 patients and a validation cohort of 468 patients. The inhospital mortality rate of the training cohort was 31.5%, and that of the validation cohort was 35.5%. As shown in Table 1 , no significant differences were observed in any of the variables between the training and validation cohorts. Table 2 , in the univariate logistic regression analysis, 23 variables were significantly related to the in-hospital mortality rate in severe thrombocytopenia (p < 0.05). Based on the multivariate logistic regression analysis with positive stepwise selection, the following were identified as independent factors for in-hospital mortality in patients with severe thrombocytopenia: age, cerebrovascular disease, malignant cancer, oxygen saturation, heart rate, respiration rate, mean arterial pressure, mechanical ventilation, vasopressors, continuous renal replacement therapy (CRRT), prothrombin time (PT), partial thromboplastin time (PTT), and blood urea nitrogen (BUN). Moreover, the variance inflation factor (VIF) of all variables was < 10, indicating no significant collinearity. Based on the results of the multivariate logistic regression analysis, we constructed a nomogram to predict the in-hospital mortality of patients with severe thrombocytopenia ( Fig. 1) . For patients aged 26 years who were prescribed vasopressor agents, underwent CRRT, and presented oxygen saturation of 93.3%; mean arterial pressure of 72 mmHg; heart rate of 112 bpm; respiration rate of 21 bpm; PT of 25 s; PTT of 36 s; and BUN of 58 mg/dL, the total number of points on the nomogram was 208, which predicted an in-hospital mortality of 0.601. The C-indices of the nomogram in the training and validation cohorts were 0.846 (95% confidence interval [CI]: 0.806-0.886) and 0.828 (95% CI: 0.791-0.865), respectively, indicating excellent prediction performance. In addition, the Hosmer-Lemeshow goodness-of-fit test indicated that the predicted outcome of the patient agreed with the actual outcome (Fig. 2) . To compare the predictive performance of the nomogram model and the most commonly used severity assessment scales in the ICU, we constructed receiver operating characteristics (ROC) curves (Fig. 3) . The areas under the ROC curve of the nomogram in the training and validation queues were higher than those of sequential organ failure assessment (SOFA) score and simplified acute physiology score II (SAPS II). In addition, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) reflected the intuitive improvement in the nomogram. Compared with those of the SOFA score, the NRI and IDI values of the nomogram in the training cohort were 0.272 (95% CI = 0.190-0.355) and 0.177 (95% CI = 0.148-0.206), respectively, whereas those in the validation cohort were 0.250 (95% CI = 0.129-0.371) and 0.129 (95% CI = 0.087-0.171), respectively. Compared with SAPS II, the NRI and IDI values of the nomogram in the training cohort were 0.282 (95% CI = 0.201-0.362) and 0.137 (95% CI = 0.107-0.166), respectively; in the validation cohort, they were 0.168 (95% CI = 0.061-0.276) and 0.078 (95% CI = 0.039-0.117), respectively. Clinical application of the nomogram. As shown in Fig. 4 , regardless of the cohort (training or validation), a threshold probability between 0.1 and 0.75 indicated that our constructed nomogram model showed more net benefits than the SOFA score and SAPS II. For example, according to Fig. 4a , if the risk threshold is set to 0.4 when we individualize and intensify care for patients with a greater than 40% risk of in-hospital mortality, for every 100 patients who use the nomogram model, 14 gain benefits without incurring losses to others. In contrast, only eight and ten patients using the SOFA score and SAPS II achieved a net benefit, respectively. Patients with thrombocytopenia are at an increased risk of bleeding, and even a mild or moderate decrease in platelet count in critically ill patients cannot be ignored 11 . Severe thrombocytopenia can no longer be regarded as a comorbidity of other diseases, as it severely affects patient management and restricts doctors from providing invasive intervention. Therefore, it was necessary to develop a specific model to predict patient prognosis. To the best our knowledge, this is the first study to develop and validate a predictive model for in-hospital mortality in patients with severe thrombocytopenia. We revealed the following as independent risk factors for in-hospital mortality in patients with severe thrombocytopenia: age, presence of cerebrovascular disease, malignant cancer, oxygen saturation, heart rate, respiration rate, mean arterial pressure, mechanical ventilation, vasopressor use, CRRT; PT, PTT, and BUN. These risk factors were used to construct a nomogram model, which was verified and evaluated using ROC curve, calibration curve, IDI, NRI, and decision curve analysis. We believe that the nomogram model has good predictive performance and clinical application value. In the prognostic models of different diseases, age, as a non-interventional factor, has been reported as an independent risk factor for most diseases 12, 13 . Generally, the decline in the body's immunity with age is inevitable 14 . Additionally, the existence of non-interventional factors affects the net benefit of clinical intervention based on this nomogram. www.nature.com/scientificreports/ Vital signs beyond the normal range indicate that the body is making compensatory adjustments. However, once out of control, the patients' condition becomes serious, and clinical interventions, such as mechanical ventilation and vasopressor agents, are necessary. Comorbidities affect patients in many ways, and this may be due to the absence of platelet homeostasis. Cardiovascular diseases often require antiplatelet therapy, which contradicts the treatment of patients with severe thrombocytopenia 15 . The immune system of patients with malignant tumors is severely damaged, and thrombocytopenia is the main side effect of cancer treatment 16 . From the nomogram model, it is clear that PT, PTT, and BUN are laboratory parameters worth paying attention to. PT and PTT reflect the function of the coagulation system. Patients with severe thrombocytopenia have microvascular failure, organ dysfunction, and blood coagulation disorders which increase the risk of bleeding 17 . BUN is an indicator of renal function; studies have found that it is an independent risk factor for the prognosis of critically ill patients, especially those with cardiogenic diseases 18, 19 . Our study found that BUN is independently related to the prognosis of patients with severe thrombocytopenia, possibly because patients with renal failure also have complex hemostatic disorders 20,21 . Established nomogram for predicting the risk of in-hospital mortality in patients with severe thrombocytopenia. Each variable value corresponds to the "point" axis to determine the corresponding score, and the total points were obtained by adding the scores of all variables. To determine the risk of in-hospital mortality, locate the total score on the "total score" axis, and follow vertically downwards to find the risk of in-hospital mortality on the "risk" axis. *p < 0.05, * *p < 0.01 and ***p < 0.001. www.nature.com/scientificreports/ A nomogram is a common visual presentation for predictive models. Compared with the traditional critically ill patient scoring systems (SOFA and SAPS II), risk stratification based on our nomogram model showed a higher clinical benefit. However, our findings had some limitations. First, because the data were extracted from the MIMIC-IV database, though the nomogram model passed verification in the validation cohort, its universality is limited, and verification using multi-center data is required. Second, because of the retrospective study design, potential selection bias is unavoidable, and the results should be interpreted with caution. Third, it is undeniable that there may be variables that were not included in the model, which have significant effects on in-hospital mortality in patients with severe thrombocytopenia. Fourth, the nomogram model comprises multiple variables and, in practice, it is not suitable for patients with missing variables. Therefore, we recommend deciding whether to use this model to assess patient outcomes either before or at the time of patient admission to the ICU, as opposed to waiting for more than 24 h following admission. Although the nomogram model could provide an important reference for clinical diagnosis and treatment, it could not accurately predict prognosis. The nomogram model we developed can help quantitatively assess the prognostic risk factors in patients with severe thrombocytopenia. This model has superior predictive performance and can provide a good reference for evaluating the in-hospital mortality rate in patients with severe thrombocytopenia. However, multi-center prospective studies are required for further verification. Fig. 1 ). Data extraction. We used the structured query language in PostgreSQL 10 software to extract clinical data from the final study population. The following data were collected (results of multiple measurements were included as the initial value or the mean value as required): (1) demographics including age, sex, and weight measured for the first time after ICU admission; (2) and R (version 4.1.0) software were used for data analysis and drawing graphs. In the training cohort, univariate logistic regression analysis was initially used to screen for candidate variables. Then, according to the value minimization principle of Akaike information criteria 24 , multivariate logistic regression analysis with positive stepwise selection was performed on the statistically significant variables (p < 0.05). The VIF tested the collinearity between variables, whose acceptable range is < 10 25 . The "rms" package of R software was used to convert the results into nomograms. To test the clinical application performance of the model, both the training and validation cohorts were drawn with ROC curves. The performance of the SOFA score and SAPS II were compared with the performance of the new model. We used the NRI and IDI to more objectively evaluate the improvement of the new model 26 . The calibration curve used the Hosmer-Lemeshow goodness-of-fit test to reflect the consistency between the actual and the predicted probabilities 27 . Decision curve analysis was performed to assess the net clinical benefits of the new model under different thresholds 28 . The outcome variable was all-cause in-hospital mortality in patients with severe thrombocytopenia. This study was based on the transparent reporting of individual prognosis or diagnosis (TRIPOD) of multivariate predictive models guidelines for analysis and reporting 29 . Ethics approval and consent to participate. The collection of raw data, as well as the establishment of the database, was approved by the Institutional Review Board of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology (Cambridge, MA). Since all medical data were deidentified, the requirement for individual patient consent was waived. In accordance with the data usage agreement, Lu passed the training for protecting human research participants. All available data were obtained from MIMIC-IV database. The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author/s. 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Q.Z. contributed to the literature search, drawing of diagrams, and drafting of the manuscript. J.J. contributed to data analysis and software support. This study did not receive any grant from funding agencies. The authors declare no competing interests. The online version contains supplementary material available at https:// doi. org/ 10. 1038/ s41598-022-10438-y.Correspondence and requests for materials should be addressed to Y.L.Reprints and permissions information is available at www.nature.com/reprints.Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.